text:
\section{Introduction}
\label{sec:introduction}

\let\thefootnote\relax\footnotetext{This work was supported by the Amazon Research Awards (ARA) and the Toyota Research Institute (TRI).}
\let\thefootnote\relax \footnotetext{Corresponding author: Francois Hogan \texttt{$<$fhogan@mit.edu$>$}
}

\let\thefootnote\relax\footnotetext{Project website: \href{https://mcube.mit.edu/research/tactile_dexterity.html}{https://mcube.mit.edu/research/tactile\underbar{ }dexterity.html}}

\let\thefootnote\relax\footnotetext{Project video: \href{https://youtu.be/f59FoS-hV7c}{https://youtu.be/f59FoS-hV7c}}

This paper studies the use of tactile sensing for dexterous robotic manipulation, or \emph{tactile dexterity}.
Despite the evidence that humans heavily depend on the sense of touch to manipulate objects~\cite{johansson1984roles}, robots still rely mostly on visual feedback. The vision-based approach has been effective for tasks such as pick-and-place \cite{correll2016analysis} but it presents fundamental limitations to accomplish dexterous manipulation tasks that depend on more accurate and controlled contact interactions, such as object reorientation, object insertion, or almost any kind of object use.

The mechanics of object manipulation are driven primarily by the relative motions and forces at the frictional interfaces between object, end-effectors and environment.
As a result, we believe that tactile sensors, with their ability to localize contact geometry, detect contact motion, and infer contact forces, should be at the center of our manipulation plans.

Our approach to \emph{tactile dexterity}, described in Sec.~\ref{sec:approach}, is based on planning for robot/object interactions that render interpretable tactile information for control.
At its core, this is an approach to robotic manipulation that puts tactile feedback at the center to bypass some of its common caveats.
Tactile information is by nature local and, in general, not sufficient to fully describe the state of a manipulated system~\cite{koval2015pose}. Furthermore, the design of feedback policies for systems undergoing physical contact is challenging, even when full state feedback is available~\cite{woodruff2017planning,hogan2018reactive}.

\begin{figure*}[t]
\centering{\includegraphics[width=17.4cm]{figures/tactile_dexterity_approach_flipped.png}}
\caption{{\small \textbf{Planning to pivot a box.} Pivoting a box involves a hierarchy of planing problems. Tactile sensors allow us to observe and control contact points. The trajectory of contact points is determined by the end-effector pose trajectory, which in turn is generated by the joint trajectory of the robot arm.
We propose an approach to dexterous manipulation that structures the planning problem in a way that when unexpected behavior of the contact points is observed with the tactile sensors, the robot can replan the motion of the contact points, palms, and arms in real-time.}}
\vspace{-0.15in}
\label{fig:tactile_dexterity}
\end{figure*}

Given these challenges, the key question becomes: How do we structure manipulation planning so that tactile feedback is relevant and convenient?
Our approach to tactile dexterity:
\begin{itemize}

    \item \textbf{Targets contact interactions} that produce tactile imprints rich in geometric features (for estimation); and that define dynamic systems with simple mechanics and efficient closed-loop policies (for control). In Sec.~\ref{sec:mechanics} we describe four such manipulation primitives: \texttt{grasp}, \texttt{push}, \texttt{pivot}, and \texttt{pull}.

     \item \textbf{Partitions the role of tactile control} into controlling the \emph{contact state} between end-effector and object; and controlling the \emph{object state} in its environment. We describe a formulation for both in Sec.~\ref{sec:tactile_control}.
\end{itemize}

We present a first study of this approach for the scenario of a dual-arm robot equipped with high-resolution tactile-sensing palms (based on GelSlim~\cite{donlon2018gelslim}) as end-effectors, and tasked with manipulating an object on a table-top from an arbitrary initial pose to an arbitrary target pose. An offline graph-search task planner, described in Sec.~\ref{sec:planning}, sequences manipulation primitives, which are   executed in a closed-loop fashion by the robot.
The focus of the experiments described in Sec.~\ref{sec:results} is to evaluate the robustness of the system to external perturbations and to uncertainty in the initial pose of the object.

\section{Related Work}
\label{sec:related_work}

This section outlines previous relevant research in exploiting tactile sensing for manipulation control.

\myparagraph{Slip Control} Veiga~\cite{veiga2015stabilizing} learns a predictor of slippage of an object in a grasp. This model is  used in~\cite{veiga2018hand} to maintain stable multi-fingered grasps under external perturbations, where each finger acts independently to enforce sticking contact. Dong~\cite{dong2019} develops an incipient slip detection algorithm with a GelSlim vision-based tactile sensor~\cite{donlon2018gelslim}, and uses it to design a closed-loop tactile controller that maintains stable grasps in prehensile experiments such as a cap into a bottle with a parallel-jaw gripper. Li~\cite{li2014learning} develops an impedance controller that adapts the grasp on an object  when it is predicted to be unstable. These studies focus on controlling stick/slip interactions but do not explicitly control the trajectory of the manipulated object.

\myparagraph{Pose Control}
Tian~\cite{tian2019manipulation} trains a deep convolutional neural network  to predict the motion of a ball rolled on the ground using a tactile finger directly in tactile space. This model is controlled using sample-based MPC.  A drawback of this approach is its need for large quantities of real-world data, which would be challenging to collect for the rich palm/objects interactions considered in this paper.

Li~\cite{li2014localization} shows that localized object features can be exploited with tactile sensing to recover an accurate estimate of  its pose.  This strategy has been shown to be effective at performing challenging manipulation tasks such as part insertion with small tolerances.  Izatt~\cite{izatt2017tracking} fuses tactile and visual perception by interpreting tactile imprints  as local $3$D pointclouds  within a Kalman filter framework for object pose estimation.   More recently,  Bauza~\cite{bauza2019tactile} develops a tactile based pose estimation algorithm that exploits a high resolution tactile map of the object to localize tactile imprints.

\myparagraph{Dexterous Manipulation}
Erdman  \cite{erdmann1998exploration} first studied the use of robotic palms   as a way to achieve rich manipulation skills  by exploiting the mobility of  robotic manipulators. Researchers have investigated robotic palms for scooping \cite{trinkle1988investigation, trinkle1992stability}, tilting \cite{erdmann1988exploration},  grasping \cite{paljug1993experimental, yun1993object}, and collaborative manipulation \cite{bicchi1995mobility, bicchi1995dexterous}. This paper draws inspiration from  \cite{kaelbling2010hierarchical, Woodruff_ICRA_2017, toussaint2018differentiable} by structuring complex manipulation behavior as a combination of simpler manipulation primitives.

\begin{figure*}[t]%
\centering
\subfigure[
]{%
 \includegraphics[height=3.5cm]{figures/grasping_fbd.png}}
 \label{fig:grasping_fbd}%
 \hspace{0mm}
 \subfigure[
 ]{%
 \includegraphics[height=3.5cm]{figures/pushing_fbd.png}}
 \label{fig:pushing_fbd}%
 \hspace{0mm}
 \subfigure[]{%
 \includegraphics[height=3.5cm]{figures/levering_fbd.png}
  \label{fig:levering_fbd}
  }
 \hspace{0mm}
  \subfigure[

  ]{%
 \includegraphics[height=3.3cm]{figures/pulling_fbd.png}}
 \label{fig:pulling_fbd}%
\caption{{\small \textbf{Manipulation primitives}. a) \texttt{grasp}: the palms align as a parallel-jaw gripper to grasp an object. b) \texttt{push}: one  palm contacts the object laterally to manipulate it within the plane. c) \texttt{pivot}: the object is rotated about an edge on the table by both palms. d) \texttt{pull}: one palm presses down on the top of the object to slide it within the support plane.}}
\vspace{-0.10in}
\label{fig:manipulation_primitives}
\end{figure*}

\section{Approach}
\label{sec:approach}

This paper develops closed-loop tactile controllers that enable robust manipulation behavior, where the robot can react to external object perturbations. We propose an approach that divides the role of tactile control into:
\begin{itemize}
\item \textbf{Contact state control}. Enforce a desired contact formation (contact/no-contact, stick/slip) between the end effector and the object.
\item \textbf{Object state control}. Control the motion of the object using a contact based state estimator for tactile tracking and iterative replanning of the trajectories of the object, end-effector and robot.
\end{itemize}
This paper considers the scenario of manipulating an object from an initial pose to a target pose on a table top. We consider a robotic platform that is 1) dexterous, where both robotic palms are controlled independently, and 2) tactile sensorized, where each palm is equipped with high-resolution tactile sensors (based on GelSlim technology \cite{donlon2018gelslim}).
We formulate the manipulation problem as a sequencing of manipulation primitives, where each primitive is designed to have a prescribed contact interaction between the robotic palms, the object, and the environment. Fig.~\ref{fig:manipulation_primitives} shows the four manipulation primitive considered in this paper: \texttt{grasp}, \texttt{push}, \texttt{pivot}, and \texttt{pull}.
This structuring of the manipulation problem into simpler behaviors gives us the freedom to design interactions for which we understand the mechanics,  are able to interpret the tactile information, and can develop effective planning algorithms.

\section{Mechanics of Manipulation Primitives}
\label{sec:mechanics}

This section describes the mechanics of the four manipulation primitives in Fig.~\ref{fig:manipulation_primitives}. To model the palm-object-environment interaction we assume:
\begin{itemize}
    \item Known geometry of object, robot, and environment.
    \item Known coefficients of friction.
    \item Rigid-body interaction.
    \item Coulomb's friction law.
    \item Quasi-static interaction.
    \item Surface contacts with uniform pressure distribution.
\end{itemize}
These simplifications will help in designing fast trajectory planning algorithms in Sec.~\ref{sec:planning}.
The unmodeled aspects of the interactions (non-uniform pressure distributions, inertial forces, deformation of contact, etc.) are addressed by designing closed-loop tactile controllers in Section~\ref{sec:tactile_control} that react to undesired contact events such as slip.

Assuming quasi-static interactions, force equilibrium dictates that contact forces on the object (applied by the end-effector or environment) are balanced by:
\begin{equation}
\sum_{i=1}^C \mbf{G}_i(\mbf{q})^\trans\mbf{w}_i = \mbf{w}_{ext},
\label{eq:static_equilibrium}
\end{equation}
where $\mbf{q} = \bma{ccc} \mbf{p}_{o}^{\trans}&
\mbf{p}_{p,l}^\trans&\mbf{p}_{p,r}^\trans\end{array}\right]^\trans$ is the concatenation of the object pose and the left/right palm poses, $\mbf{w}_i=[\mbf{c}_i^\trans\,\, \mbs{\tau}_i^\trans]^\trans$ is the  applied wrench on the object by the $i^{th}$ contact in the contact frame, $\mbf{w}_{ext}$ is the external force applied by gravity in the world frame,  $\mbf{G}_i$ is a grasp matrix transforming the coordinates of a contact wrench from the contact frame to the world frame \cite{siciliano2016springer}, and $C$ is the number of contacts.

Contact forces are constrained within the friction cone in accordance to Coulomb's frictional law. Denoting the normal and tangential components of the contact force as $\mbf{c}_i = \bma{cc}f_{n,i}&\mbf{f}_{t,i}^\trans\end{array}\right]^\trans$, we express Coulomb's frictional law as:
\begin{eqnarray}
{f}_{n,i}  &\geq& {0}
\label{eq:coulomb_normal}\\
\abs{\mbf{f}_{t,i}}  &\leq& \mu\abs{{f}_{n,i}}.
\label{eq:coulomb_tangential}
\end{eqnarray}
In the case of point contact interactions (\texttt{grasp} and \texttt{pivot}), the contact is unable to sustain frictional moments, implying $\mbs{\tau}_i=\mbf{0}$. For contacts modeled using surface contacts (\texttt{push} and \texttt{pull}), the surface is able to resist a certain amount of frictional moment. We model surface contacts using the limit surface~\cite{goyal1991planar}, which describes the set of forces and moments that can be transmitted through the contact. In practice, we make use of the ellipsoidal approximation to the limit surface introduced in~\cite{howe1996practical} that gives a simple analytical representation of the limit surface.
A detailed description of the mechanics of similar manipulation primitives can be found in \cite{hogan2018reactive} for non-prehensile pushing, \cite{chavan2018hand} for prehensile pushing, and \cite{hou2018fast} for pivoting.

\section{Tactile Control}
\label{sec:tactile_control}

In this section, we describe tactile-based controllers that give robust behavior to the manipulation primitives.
We use GelSlim
~\cite{donlon2018gelslim}, an optical-based tactile sensor that renders high-resolution images of the  contact surface geometry and strain field, as shown in Fig.~\ref{fig:state_observer}, and in the video \href{https://youtu.be/f59FoS-hV7c}{https://youtu.be/f59FoS-hV7c}.

A primary goal of the tactile controller is to enforce a desired contact state between the palms and the object. For example, during the pivot maneuver in Fig.~\ref{fig:levering_fbd}, we want both palms to maintain sticking contacts with the corners of the object. By monitoring incipient slippage, we design a controller that engages when necessary to regulate the applied forces on the object to prevent further slip. We refer to this as \textit{Contact State Control (Sec.~\ref{sec:contact_mode_control})}. This is illustrated in Fig.~\ref{fig:tactile_controller}, where the palms rotate and apply additional normal force on the object in reaction to a slip event.

An important consequence of undesired slippage is that the position of the object has deviated from the desired trajectory. To address this, we design a tactile-based state estimator, illustrated in Fig.~\ref{fig:state_observer}, that tracks in real-time local features on the object and uses the updated object pose estimate to continuously replan object, palm, and robot arm trajectories for the manipulation primitive. We refer to this as \textit{Object State Control (Sec.~\ref{sec:contact_state_control})}

\subsection{Contact State Control}
\label{sec:contact_mode_control}
Each primitive assumes a particular contact formation between the palms and the object. This assumption is likely to be broken as unmodeled perturbations are applied on the system and cause undesired slippage. We design a contact state controller that acts to enforce the planned contact modes by reacting to a binary incipient slip signal $s_i\in\{0,1\}$ at contact $i$, based on  \cite{dong2019}.
\begin{figure}[t]%
\centering
{%
 \includegraphics[height=7.5cm]{figures/tactile_controller.png}}%
\caption{{\small \textbf{Contact state control}. How should the robot react to undesirable slippage? We design a model based tactile controller that determine locally optimal robot adjustments to recover from contact state deviations.}}
 \label{fig:tactile_controller}
\end{figure}

Coulomb friction states that slippage occurs when the contact force lies on the boundary of the friction cone, as shown in Fig.~\ref{fig:stability_margin}. Given an undesired slippage signal, we find local palm configuration and force adjustments that result in a more stable sticking solution (i.e. contact forces nearer to the center of the friction cone). The stability margin $\phi$ in Fig.~\ref{fig:stability_margin} is defined as the shortest distance from the contact force to the friction cone boundary and evaluates the risk of slipping of a particular contact. The goal of the controller is to find robot adjustments that either maximize (for sticking) or minimize (for sliding) the stability margin of a contact to enforce a desired contact mode.
In this paper we develop primitives that restrict to sticking contacts between the gripper and the object.

Given the slippage signal ${s}_i\in\{0,1\}$  at each contact and the current robot pose configuration $\mbf{q}_p = [\mbf{p}_{p,l}^\trans\,\,\mbf{p}_{p,r}^\trans]^\trans$, we search for a robot palm pose adjustment $\Delta \mbf{q}_p$ and contact force $\mbf{w}_i$ that maximize the stability margin of a particular contact $i$ (indicated by the weight $\beta_i$) by solving:
\begin{equation}
\begin{aligned}
& \underset{\Delta\mbf{q}_p, \mbf{w}_i}
{\text{max}}
& &  \beta_i \phi_i \\
 \text{s.t.}
 &&&\sum_{i=1}^C \mbf{G}_i(\mbf{q}_p + \Delta\mbf{q}_p)^\trans\mbf{w}_i = \mbf{w}_{ext} \\
&&&f_{n,i} \geq 0\\
&&& \norm{\mbf{f}_{t,i}}_2 \leq \mu \abs{f_{n,i}}\\
\end{aligned}
\label{eq:nonlinear_tactile_controller}
\end{equation}
with $\mbf{G}_i$, $\mbf{q}_p$,
$\mbf{w}_{ext}$ as defined in Section~\ref{sec:mechanics} and where $\phi$ is the stability margin shown in Fig.~\ref{fig:stability_margin}. The notation $\mbf{q}_p + \Delta \mbf{q}_p$ is abused here for simplicity, where we ensure that $\mbf{q}_p + \Delta\mbf{q}_p$ belongs to $SE(3)$ and does not violate the kinematics (i.e. maintaining contact and non-penetration) of the  contact formation.
For example, in Fig.~\ref{fig:levering_fbd}, the palms can press harder and/or pivot about the contact edge. The parameter $\beta_i$ is a weight used to biased the effort of the regulating behavior to contact $i$ based on the slip signal $s_i$.

The optimization program in ~\eqref{eq:nonlinear_tactile_controller} is non-convex because $\phi$ is nonlinear and because the constraint associated with static equilibrium is bilinear. In \cite{aceituno2017simultaneous}, the surrogate stability margin $\alpha$ illustrated in Fig.~\ref{fig:stability_margin} is proposed as a convex approximation to $\phi$. We relax then the optimization program using the surrogate margin $\alpha$ and linearizing the static equilibrium equation \eqref{eq:static_equilibrium} about the current robot configuration $\mbf{q}_p^\star$ and contact forces ${f}_{n,i}^\star$, $\mbf{f}_{t,i}^\star$ for computational efficiency. The linearized contact state controller becomes:
\begin{eqnarray}
\begin{aligned}
& \hspace{-2mm}\underset{\Delta\mbf{q}_p, \mbs{\Delta}\mbf{w}_i, \alpha_i}
{\text{max}}
& &  \hspace{-3mm}\beta_i \alpha_i \\
 \text{s.t.}
 &&&\hspace{-13mm}\sum_{i=1}^C \left(\left.
 \frac{\partial \left({G}_i \mbf{w}_i\right)}{\partial \mbf{q}_p}\right|_{\star}\mbs{\Delta}\mbf{q}_p
+ \left.
 \frac{\partial \left({G}_i \mbf{w}_i\right)}{\partial \mbf{w}_i}\right|_{\star}\mbs{\Delta}\mbf{w}_i \right) = \mbf{w}_{ext} \\
&&&\hspace{-13mm}f_{n,i} \geq 0\\
&&&\hspace{-13mm}\alpha_i \geq 0 \\
&&& \hspace{-13mm}\norm{\mbf{f}_{t,i}}_2 \leq \mu {f_{n,i}}\\
&&& \hspace{-13mm}\norm{\mbf{f}_{t,i}}_2 \leq \mu \abs{f_{n,i} - \alpha_i},
\end{aligned}
\hspace{-15mm}\label{eq:linear_tactile_controller}
\end{eqnarray}
where the symbol $(\cdot)^\star$ is used to  evaluate a term at the nominal configuration. Note that the surrogate margin $\alpha$ is not invariant to scale, i.e., we could artificially increase the margin simply by scaling up the magnitude of all the forces. To regulate this behavior, in practice, we add bound constraints to the allowable $\Delta\mbf{q}$ and $\mbs{\Delta}\mbf{w}$.
The optimization program in~\eqref{eq:linear_tactile_controller} takes the form of a convex quadratic program under a polyhedral approximation to the friction cone \cite{stewart1996implicit}.

\begin{figure}[t]%
\centering
 \includegraphics[height=3.3cm]{figures/stability_margin.png}
\caption{{\small The stability margin $\phi$ evaluates how close a contact is to the slipping boundary. The goal of the contact state controller is to maximize/minimize the stability margin to encourage/discourage  slippage. }}
 \label{fig:stability_margin}%
\end{figure}

\subsection{Object State Control}
\label{sec:contact_state_control}

The contact state controller in Sec.~\ref{sec:contact_mode_control} regulates the applied forces on the object to enforce a desired contact mode. This tactile controller reacts to fight against external perturbations but does not have the ability to change the planed trajectory of the object in response to a perturbation. To address this, we design an object state controller running in parallel  tasked with replanning object/palm trajectories to drive the object to  its target location.

In this paper we track two types of features: points (corners of the object) and lines (edges of the object).
We formulate the tactile object state estimator as an optimization program that updates the pose ${\mbf{p}}_o$ of the object to satisfy the geometric constraints associated with the tactile features, as shown in Fig.~\ref{fig:state_observer}. We quantify the error between the previous and the updated pose estimate using the distance  $d_{TS}(\mbf{p}_o, \mbf{p}_o^\star)$, where $d_{TS}$ is defined as the weighted sum of the Euclidean
metric in $\mathbb{R}^3$ and the great circle  angle metric in ${SO}(3)$ for the respective components~\cite{holladay2016distance}. We enforce that detected lines are collinear with their associated edge on the object mesh and that the sensed points are coincident with the object corner. In addition to the detected geometric constraint, we constrain the estimated object pose to satisfy the geometric constraints consistent with the current manipulation primitive.
For example, for the \texttt{pull} primitive, we enforce that the bottom surface of the object is in contact and aligned with the table top.

The estimated object pose is used to update the nominal robot palm pose trajectory which allows the robot to adapt to local object perturbations. This is further described in Sec.~\ref{sec:trajectory_planning}.

\begin{figure}[t]
\centering
{
			\includegraphics[width=8.3cm]{figures/object_pose_estimation.png}
}
\caption{{\small \textbf{Tactile object localization}. By localizing descriptive object geometric features (lines, points), we can track the pose of the object.}}
\label{fig:state_observer}
\end{figure}

\begin{figure*}[t]%
\centering
{%
 \includegraphics[width=18cm]{figures/experiments_regulation.png}}
 \label{fig:regulation_experiments
  }
\caption{{\small \textbf{Closed-loop evaluation of the tactile controller}. We consider the task of maintaining the object in a stationary pose under external perturbations. When perturbed, the contact state controller increases the normal force on the object to prevent slippage while the contact state controller replans robot motions to bring the object back to its nominal pose.}}
\label{fig:regulation_experiments}
\end{figure*}

\section{Planning}
\label{sec:planning}

We formulate the manipulation planning problem as a sequencing of manipulation primitives, as described in Sec.~\ref{sec:mechanics}. This section briefly describes an approach for how to 1) sequence the manipulation primitives to achieve the task and 2) plan robot trajectories within a manipulation primitive a to achieve a desired object transformation.

\subsection{Search for Primitive Sequence}
\label{sec:task_planning}
We formulate the search for the primitive sequence as a graph search problem. We adapt the regrasp graphs developed in~\cite{wan2015improving, hou2018fast} to include a broader set of manipulation primitives. The nodes of the manipulation graph represent possible object stable placements  and the edges represent manipulation primitive actions transforming the object from a stable placement to another. We use Dijkstra's algorithm to search for the shortest path within the constructed graph achieving the desired pose to pose reconfiguration. In the experiment in the title figure, the primitive sequence \texttt{pull$\rightarrow$pivot$\rightarrow$push} is found to achieve the desired object transformations. We refer the reader to~\cite{wan2015improving} and the project website \href{https://mcube.mit.edu/research/tactile_dexterity.html}{https://mcube.mit.edu/research/tactile\underbar{ }dexterity.html} for more details on the graph search planning framework.

\subsection{Trajectory Planning}
\label{sec:trajectory_planning}

After the graph search planner has determined a sequence of primitives, we compute  robot/object trajectories independently for each primitive. We rely on primitive specific planners
for which we require that can regenerate trajectories at high frequency to exploit the timely estimated of object pose from the tactile object tracker from  Section~\ref{sec:contact_state_control}.

For \texttt{grasp} and \texttt{pull}, we plan object motions that kinematically stick to the palms. For these, we directly interpolate between the initial and final poses of the object to determine the palm pose trajectory. For \texttt{pivot}, we compute an interpolated object trajectory between the initial and final poses about a specified center of rotation.  Solving~\eqref{eq:static_equilibrium}, we find the palm pose relative to the object that maintains a sticking interaction at all contacts. For \texttt{push}, we use a Dubins' car planner that computes the time-optimal trajectory  connecting  the initial and final object configurations with a single push based on~\cite{zhou2017pushing}.
We consider pushes on all sides of the object and execute the trajectory with the shortest path.

\section{Results}
\label{sec:results}

We validate our approach to \textit{tactile dexterity} on an ABB YuMi dual-arm robot with real experiments visualized in \href{https://youtu.be/f59FoS-hV7c}{https://youtu.be/f59FoS-hV7c} that evaluate the ability of the tactile controller to handle external perturbations on a table top manipulation task.

The title figure shows snapshots of an experiment where the robot manipulates an object from an initial pose  $\mbf{q}_0 = [0.3,\,\,
-0.2,\,\,
 0.07,\,\,
0.38,\,\,
 0.60,\,\,
 0.60,\,\,
0.38]^\trans$ to a target pose $\mbf{q}_f = [ 0.45,\,\,
0.3,\,\,
0.045,\,\,
0.0,\,\,
0.71,\,\,
0.0,\,\,
0.71 ]^\trans$. To achieve the task, the robot follows the  sequence: \texttt{pull} the object to the middle of the table, \texttt{pivot} the object to its target placement, and \texttt{push} it to its target location. The initial \texttt{pull} primitive is necessary to move the object to a location that allows the robot to perform a pivot manoeuvre with well defined inverse kinematics and that avoids collisions with the environment.

In Fig.~\ref{fig:regulation_experiments}, we evaluate the closed-loop performance of the tactile controller in Section~\ref{sec:tactile_control} on individual primitives. We illustrate the regulation task of maintaining an object in a stationary pose for the \texttt{pull} and \texttt{grasp} primitives. The regulation task allows to better visualize the reactive capabilities of the controller without loss of generality. In each experiment, we apply two successive impulsive forces on the object and evaluate  the stabilizing capabilities of the tactile controller.
Figure~\ref{fig:regulation_experiments} plots the mean and standard deviation of the error between the object's desired and measured poses for $5$ consecutive experiments. In both cases, the controller quickly reacts to the disturbance by 1) detecting slippage events at the contact interfaces and 2) tracking the pose of the object using the detected object edge in the tactile signal. First, the applied normal force is increased following \eqref{eq:linear_tactile_controller} in reaction to the detected slippage at the contact interface. Second, the robot replans its trajectory from updates on the object state and this quickly returns to its nominal pose.  For evaluation purposes, we track the ground truth pose of the object using an Apriltag visual marker~\cite{olson2011apriltag}

\section{Discussion}
\label{sec:discussion}

This paper introduces \textit{tactile dexterity}, an approach to dexterous manipulation that exploits tactile sensing for reactive control.
By structuring the manipulation problem as a sequence of manipulation primitives that render interpretable tactile information, we enable tactile object state estimation and tactile object control, yielding robust manipulation behavior.
This requires targeting primitive interactions to those that have simple rigid body mechanics models and efficient planners.
We show the ability of the tactile controllers to modulate contact forces, track the pose of the object, and handle external perturbations by replanning in real-time.

This research relies on a number of important assumptions about the structure of the problem:
\begin{itemize}
    \item Known environment. The manipulation primitives and planning algorithms are tailored to a flat surface environment.
    %
    \item Manipulation primitives. We use human intuition to develop a library of manipulation primitives that are amenable for planning and control (for controllability).
    \item Tactile Pose Estimation. We assume that the pose of the object can be reconstructed from tactile observations (for observability).
\end{itemize}
These assumptions facilitate the development of state estimation, planning, and control frameworks capable of exploiting tactile sensing for dexterous manipulation. They constitute both the limitations, as well as strengths of the approach.

We believe that there are natural extensions to generalize this approach to dexterous manipulation that puts tactile feedback at the center:

\myparagraph{General Manipulation Primitives.}
Developing manipulation primitives with richer contact interactions (contact switches, stick/slip interactions, etc.) will allow more expressive behavior. Due to increased complexity of the mechanics of these primitives, they will likely benefit even more from real-time tactile observations of contact.
This line of research will requires controller architectures that can reason across hybrid contact switches, e.g. developed in \cite{hogan2016feedback, doshi2019hybrid}.

Another interesting possibility is to learn manipulation primitives from experience, and defining a primitive, broadly speaking, as a behavior for which the robot can learn a stable controller. This would potentially reduce the need for human intuition.

\myparagraph{Object tracking.}
A formal approach to characterizing object observability directly from tactile feedback could assist with identifying discriminative features and grasps of the object. Furthermore, combining touch with vision is a natural extension of this research that would allow the robot to recover from larger object disturbances.

\myparagraph{Task-and-Motion Planning}. Dealing with more complex environments and tasks (e.g., pick up the box and put it on a shelf, or inside a closed drawer), would require to integrate the system with a high level task planner that can sequence manipulation primitives, as is commonly done in task and motion planning, while still being mindful of the mechanics of contact \cite{toussaint2018differentiable, holladay2019force}. Ideally, these planners would be reactive and allow the robot to switch primitives in response to unexpected deviations.

{\small
\section{Introduction}
\label{sec:introduction}

\let\thefootnote\relax\footnotetext{This work was supported by the Amazon Research Awards (ARA) and the Toyota Research Institute (TRI).}
\let\thefootnote\relax \footnotetext{Corresponding author: Francois Hogan \texttt{$<$fhogan@mit.edu$>$}
}

\let\thefootnote\relax\footnotetext{Project website: \href{https://mcube.mit.edu/research/tactile_dexterity.html}{https://mcube.mit.edu/research/tactile\underbar{ }dexterity.html}}

\let\thefootnote\relax\footnotetext{Project video: \href{https://youtu.be/f59FoS-hV7c}{https://youtu.be/f59FoS-hV7c}}

This paper studies the use of tactile sensing for dexterous robotic manipulation, or \emph{tactile dexterity}.
Despite the evidence that humans heavily depend on the sense of touch to manipulate objects~\cite{johansson1984roles}, robots still rely mostly on visual feedback. The vision-based approach has been effective for tasks such as pick-and-place \cite{correll2016analysis} but it presents fundamental limitations to accomplish dexterous manipulation tasks that depend on more accurate and controlled contact interactions, such as object reorientation, object insertion, or almost any kind of object use.

The mechanics of object manipulation are driven primarily by the relative motions and forces at the frictional interfaces between object, end-effectors and environment.
As a result, we believe that tactile sensors, with their ability to localize contact geometry, detect contact motion, and infer contact forces, should be at the center of our manipulation plans.

Our approach to \emph{tactile dexterity}, described in Sec.~\ref{sec:approach}, is based on planning for robot/object interactions that render interpretable tactile information for control.
At its core, this is an approach to robotic manipulation that puts tactile feedback at the center to bypass some of its common caveats.
Tactile information is by nature local and, in general, not sufficient to fully describe the state of a manipulated system~\cite{koval2015pose}. Furthermore, the design of feedback policies for systems undergoing physical contact is challenging, even when full state feedback is available~\cite{woodruff2017planning,hogan2018reactive}.

\begin{figure*}[t]
\centering{\includegraphics[width=17.4cm]{figures/tactile_dexterity_approach_flipped.png}}
\caption{{\small \textbf{Planning to pivot a box.} Pivoting a box involves a hierarchy of planing problems. Tactile sensors allow us to observe and control contact points. The trajectory of contact points is determined by the end-effector pose trajectory, which in turn is generated by the joint trajectory of the robot arm.
We propose an approach to dexterous manipulation that structures the planning problem in a way that when unexpected behavior of the contact points is observed with the tactile sensors, the robot can replan the motion of the contact points, palms, and arms in real-time.}}
\vspace{-0.15in}
\label{fig:tactile_dexterity}
\end{figure*}

Given these challenges, the key question becomes: How do we structure manipulation planning so that tactile feedback is relevant and convenient?
Our approach to tactile dexterity:
\begin{itemize}

    \item \textbf{Targets contact interactions} that produce tactile imprints rich in geometric features (for estimation); and that define dynamic systems with simple mechanics and efficient closed-loop policies (for control). In Sec.~\ref{sec:mechanics} we describe four such manipulation primitives: \texttt{grasp}, \texttt{push}, \texttt{pivot}, and \texttt{pull}.

     \item \textbf{Partitions the role of tactile control} into controlling the \emph{contact state} between end-effector and object; and controlling the \emph{object state} in its environment. We describe a formulation for both in Sec.~\ref{sec:tactile_control}.
\end{itemize}

We present a first study of this approach for the scenario of a dual-arm robot equipped with high-resolution tactile-sensing palms (based on GelSlim~\cite{donlon2018gelslim}) as end-effectors, and tasked with manipulating an object on a table-top from an arbitrary initial pose to an arbitrary target pose. An offline graph-search task planner, described in Sec.~\ref{sec:planning}, sequences manipulation primitives, which are   executed in a closed-loop fashion by the robot.
The focus of the experiments described in Sec.~\ref{sec:results} is to evaluate the robustness of the system to external perturbations and to uncertainty in the initial pose of the object.

\section{Related Work}
\label{sec:related_work}

This section outlines previous relevant research in exploiting tactile sensing for manipulation control.

\myparagraph{Slip Control} Veiga~\cite{veiga2015stabilizing} learns a predictor of slippage of an object in a grasp. This model is  used in~\cite{veiga2018hand} to maintain stable multi-fingered grasps under external perturbations, where each finger acts independently to enforce sticking contact. Dong~\cite{dong2019} develops an incipient slip detection algorithm with a GelSlim vision-based tactile sensor~\cite{donlon2018gelslim}, and uses it to design a closed-loop tactile controller that maintains stable grasps in prehensile experiments such as a cap into a bottle with a parallel-jaw gripper. Li~\cite{li2014learning} develops an impedance controller that adapts the grasp on an object  when it is predicted to be unstable. These studies focus on controlling stick/slip interactions but do not explicitly control the trajectory of the manipulated object.

\myparagraph{Pose Control}
Tian~\cite{tian2019manipulation} trains a deep convolutional neural network  to predict the motion of a ball rolled on the ground using a tactile finger directly in tactile space. This model is controlled using sample-based MPC.  A drawback of this approach is its need for large quantities of real-world data, which would be challenging to collect for the rich palm/objects interactions considered in this paper.

Li~\cite{li2014localization} shows that localized object features can be exploited with tactile sensing to recover an accurate estimate of  its pose.  This strategy has been shown to be effective at performing challenging manipulation tasks such as part insertion with small tolerances.  Izatt~\cite{izatt2017tracking} fuses tactile and visual perception by interpreting tactile imprints  as local $3$D pointclouds  within a Kalman filter framework for object pose estimation.   More recently,  Bauza~\cite{bauza2019tactile} develops a tactile based pose estimation algorithm that exploits a high resolution tactile map of the object to localize tactile imprints.

\myparagraph{Dexterous Manipulation}
Erdman  \cite{erdmann1998exploration} first studied the use of robotic palms   as a way to achieve rich manipulation skills  by exploiting the mobility of  robotic manipulators. Researchers have investigated robotic palms for scooping \cite{trinkle1988investigation, trinkle1992stability}, tilting \cite{erdmann1988exploration},  grasping \cite{paljug1993experimental, yun1993object}, and collaborative manipulation \cite{bicchi1995mobility, bicchi1995dexterous}. This paper draws inspiration from  \cite{kaelbling2010hierarchical, Woodruff_ICRA_2017, toussaint2018differentiable} by structuring complex manipulation behavior as a combination of simpler manipulation primitives.

\begin{figure*}[t]%
\centering
\subfigure[
]{%
 \includegraphics[height=3.5cm]{figures/grasping_fbd.png}}
 \label{fig:grasping_fbd}%
 \hspace{0mm}
 \subfigure[
 ]{%
 \includegraphics[height=3.5cm]{figures/pushing_fbd.png}}
 \label{fig:pushing_fbd}%
 \hspace{0mm}
 \subfigure[]{%
 \includegraphics[height=3.5cm]{figures/levering_fbd.png}
  \label{fig:levering_fbd}
  }
 \hspace{0mm}
  \subfigure[

  ]{%
 \includegraphics[height=3.3cm]{figures/pulling_fbd.png}}
 \label{fig:pulling_fbd}%
\caption{{\small \textbf{Manipulation primitives}. a) \texttt{grasp}: the palms align as a parallel-jaw gripper to grasp an object. b) \texttt{push}: one  palm contacts the object laterally to manipulate it within the plane. c) \texttt{pivot}: the object is rotated about an edge on the table by both palms. d) \texttt{pull}: one palm presses down on the top of the object to slide it within the support plane.}}
\vspace{-0.10in}
\label{fig:manipulation_primitives}
\end{figure*}

\section{Approach}
\label{sec:approach}

This paper develops closed-loop tactile controllers that enable robust manipulation behavior, where the robot can react to external object perturbations. We propose an approach that divides the role of tactile control into:
\begin{itemize}
\item \textbf{Contact state control}. Enforce a desired contact formation (contact/no-contact, stick/slip) between the end effector and the object.
\item \textbf{Object state control}. Control the motion of the object using a contact based state estimator for tactile tracking and iterative replanning of the trajectories of the object, end-effector and robot.
\end{itemize}
This paper considers the scenario of manipulating an object from an initial pose to a target pose on a table top. We consider a robotic platform that is 1) dexterous, where both robotic palms are controlled independently, and 2) tactile sensorized, where each palm is equipped with high-resolution tactile sensors (based on GelSlim technology \cite{donlon2018gelslim}).
We formulate the manipulation problem as a sequencing of manipulation primitives, where each primitive is designed to have a prescribed contact interaction between the robotic palms, the object, and the environment. Fig.~\ref{fig:manipulation_primitives} shows the four manipulation primitive considered in this paper: \texttt{grasp}, \texttt{push}, \texttt{pivot}, and \texttt{pull}.
This structuring of the manipulation problem into simpler behaviors gives us the freedom to design interactions for which we understand the mechanics,  are able to interpret the tactile information, and can develop effective planning algorithms.

\section{Mechanics of Manipulation Primitives}
\label{sec:mechanics}

This section describes the mechanics of the four manipulation primitives in Fig.~\ref{fig:manipulation_primitives}. To model the palm-object-environment interaction we assume:
\begin{itemize}
    \item Known geometry of object, robot, and environment.
    \item Known coefficients of friction.
    \item Rigid-body interaction.
    \item Coulomb's friction law.
    \item Quasi-static interaction.
    \item Surface contacts with uniform pressure distribution.
\end{itemize}
These simplifications will help in designing fast trajectory planning algorithms in Sec.~\ref{sec:planning}.
The unmodeled aspects of the interactions (non-uniform pressure distributions, inertial forces, deformation of contact, etc.) are addressed by designing closed-loop tactile controllers in Section~\ref{sec:tactile_control} that react to undesired contact events such as slip.

Assuming quasi-static interactions, force equilibrium dictates that contact forces on the object (applied by the end-effector or environment) are balanced by:
\begin{equation}
\sum_{i=1}^C \mbf{G}_i(\mbf{q})^\trans\mbf{w}_i = \mbf{w}_{ext},
\label{eq:static_equilibrium}
\end{equation}
where $\mbf{q} = \bma{ccc} \mbf{p}_{o}^{\trans}&
\mbf{p}_{p,l}^\trans&\mbf{p}_{p,r}^\trans\end{array}\right]^\trans$ is the concatenation of the object pose and the left/right palm poses, $\mbf{w}_i=[\mbf{c}_i^\trans\,\, \mbs{\tau}_i^\trans]^\trans$ is the  applied wrench on the object by the $i^{th}$ contact in the contact frame, $\mbf{w}_{ext}$ is the external force applied by gravity in the world frame,  $\mbf{G}_i$ is a grasp matrix transforming the coordinates of a contact wrench from the contact frame to the world frame \cite{siciliano2016springer}, and $C$ is the number of contacts.

Contact forces are constrained within the friction cone in accordance to Coulomb's frictional law. Denoting the normal and tangential components of the contact force as $\mbf{c}_i = \bma{cc}f_{n,i}&\mbf{f}_{t,i}^\trans\end{array}\right]^\trans$, we express Coulomb's frictional law as:
\begin{eqnarray}
{f}_{n,i}  &\geq& {0}
\label{eq:coulomb_normal}\\
\abs{\mbf{f}_{t,i}}  &\leq& \mu\abs{{f}_{n,i}}.
\label{eq:coulomb_tangential}
\end{eqnarray}
In the case of point contact interactions (\texttt{grasp} and \texttt{pivot}), the contact is unable to sustain frictional moments, implying $\mbs{\tau}_i=\mbf{0}$. For contacts modeled using surface contacts (\texttt{push} and \texttt{pull}), the surface is able to resist a certain amount of frictional moment. We model surface contacts using the limit surface~\cite{goyal1991planar}, which describes the set of forces and moments that can be transmitted through the contact. In practice, we make use of the ellipsoidal approximation to the limit surface introduced in~\cite{howe1996practical} that gives a simple analytical representation of the limit surface.
A detailed description of the mechanics of similar manipulation primitives can be found in \cite{hogan2018reactive} for non-prehensile pushing, \cite{chavan2018hand} for prehensile pushing, and \cite{hou2018fast} for pivoting.

\section{Tactile Control}
\label{sec:tactile_control}

In this section, we describe tactile-based controllers that give robust behavior to the manipulation primitives.
We use GelSlim
~\cite{donlon2018gelslim}, an optical-based tactile sensor that renders high-resolution images of the  contact surface geometry and strain field, as shown in Fig.~\ref{fig:state_observer}, and in the video \href{https://youtu.be/f59FoS-hV7c}{https://youtu.be/f59FoS-hV7c}.

A primary goal of the tactile controller is to enforce a desired contact state between the palms and the object. For example, during the pivot maneuver in Fig.~\ref{fig:levering_fbd}, we want both palms to maintain sticking contacts with the corners of the object. By monitoring incipient slippage, we design a controller that engages when necessary to regulate the applied forces on the object to prevent further slip. We refer to this as \textit{Contact State Control (Sec.~\ref{sec:contact_mode_control})}. This is illustrated in Fig.~\ref{fig:tactile_controller}, where the palms rotate and apply additional normal force on the object in reaction to a slip event.

An important consequence of undesired slippage is that the position of the object has deviated from the desired trajectory. To address this, we design a tactile-based state estimator, illustrated in Fig.~\ref{fig:state_observer}, that tracks in real-time local features on the object and uses the updated object pose estimate to continuously replan object, palm, and robot arm trajectories for the manipulation primitive. We refer to this as \textit{Object State Control (Sec.~\ref{sec:contact_state_control})}

\subsection{Contact State Control}
\label{sec:contact_mode_control}
Each primitive assumes a particular contact formation between the palms and the object. This assumption is likely to be broken as unmodeled perturbations are applied on the system and cause undesired slippage. We design a contact state controller that acts to enforce the planned contact modes by reacting to a binary incipient slip signal $s_i\in\{0,1\}$ at contact $i$, based on  \cite{dong2019}.
\begin{figure}[t]%
\centering
{%
 \includegraphics[height=7.5cm]{figures/tactile_controller.png}}%
\caption{{\small \textbf{Contact state control}. How should the robot react to undesirable slippage? We design a model based tactile controller that determine locally optimal robot adjustments to recover from contact state deviations.}}
 \label{fig:tactile_controller}
\end{figure}

Coulomb friction states that slippage occurs when the contact force lies on the boundary of the friction cone, as shown in Fig.~\ref{fig:stability_margin}. Given an undesired slippage signal, we find local palm configuration and force adjustments that result in a more stable sticking solution (i.e. contact forces nearer to the center of the friction cone). The stability margin $\phi$ in Fig.~\ref{fig:stability_margin} is defined as the shortest distance from the contact force to the friction cone boundary and evaluates the risk of slipping of a particular contact. The goal of the controller is to find robot adjustments that either maximize (for sticking) or minimize (for sliding) the stability margin of a contact to enforce a desired contact mode.
In this paper we develop primitives that restrict to sticking contacts between the gripper and the object.

Given the slippage signal ${s}_i\in\{0,1\}$  at each contact and the current robot pose configuration $\mbf{q}_p = [\mbf{p}_{p,l}^\trans\,\,\mbf{p}_{p,r}^\trans]^\trans$, we search for a robot palm pose adjustment $\Delta \mbf{q}_p$ and contact force $\mbf{w}_i$ that maximize the stability margin of a particular contact $i$ (indicated by the weight $\beta_i$) by solving:
\begin{equation}
\begin{aligned}
& \underset{\Delta\mbf{q}_p, \mbf{w}_i}
{\text{max}}
& &  \beta_i \phi_i \\
 \text{s.t.}
 &&&\sum_{i=1}^C \mbf{G}_i(\mbf{q}_p + \Delta\mbf{q}_p)^\trans\mbf{w}_i = \mbf{w}_{ext} \\
&&&f_{n,i} \geq 0\\
&&& \norm{\mbf{f}_{t,i}}_2 \leq \mu \abs{f_{n,i}}\\
\end{aligned}
\label{eq:nonlinear_tactile_controller}
\end{equation}
with $\mbf{G}_i$, $\mbf{q}_p$,
$\mbf{w}_{ext}$ as defined in Section~\ref{sec:mechanics} and where $\phi$ is the stability margin shown in Fig.~\ref{fig:stability_margin}. The notation $\mbf{q}_p + \Delta \mbf{q}_p$ is abused here for simplicity, where we ensure that $\mbf{q}_p + \Delta\mbf{q}_p$ belongs to $SE(3)$ and does not violate the kinematics (i.e. maintaining contact and non-penetration) of the  contact formation.
For example, in Fig.~\ref{fig:levering_fbd}, the palms can press harder and/or pivot about the contact edge. The parameter $\beta_i$ is a weight used to biased the effort of the regulating behavior to contact $i$ based on the slip signal $s_i$.

The optimization program in ~\eqref{eq:nonlinear_tactile_controller} is non-convex because $\phi$ is nonlinear and because the constraint associated with static equilibrium is bilinear. In \cite{aceituno2017simultaneous}, the surrogate stability margin $\alpha$ illustrated in Fig.~\ref{fig:stability_margin} is proposed as a convex approximation to $\phi$. We relax then the optimization program using the surrogate margin $\alpha$ and linearizing the static equilibrium equation \eqref{eq:static_equilibrium} about the current robot configuration $\mbf{q}_p^\star$ and contact forces ${f}_{n,i}^\star$, $\mbf{f}_{t,i}^\star$ for computational efficiency. The linearized contact state controller becomes:
\begin{eqnarray}
\begin{aligned}
& \hspace{-2mm}\underset{\Delta\mbf{q}_p, \mbs{\Delta}\mbf{w}_i, \alpha_i}
{\text{max}}
& &  \hspace{-3mm}\beta_i \alpha_i \\
 \text{s.t.}
 &&&\hspace{-13mm}\sum_{i=1}^C \left(\left.
 \frac{\partial \left({G}_i \mbf{w}_i\right)}{\partial \mbf{q}_p}\right|_{\star}\mbs{\Delta}\mbf{q}_p
+ \left.
 \frac{\partial \left({G}_i \mbf{w}_i\right)}{\partial \mbf{w}_i}\right|_{\star}\mbs{\Delta}\mbf{w}_i \right) = \mbf{w}_{ext} \\
&&&\hspace{-13mm}f_{n,i} \geq 0\\
&&&\hspace{-13mm}\alpha_i \geq 0 \\
&&& \hspace{-13mm}\norm{\mbf{f}_{t,i}}_2 \leq \mu {f_{n,i}}\\
&&& \hspace{-13mm}\norm{\mbf{f}_{t,i}}_2 \leq \mu \abs{f_{n,i} - \alpha_i},
\end{aligned}
\hspace{-15mm}\label{eq:linear_tactile_controller}
\end{eqnarray}
where the symbol $(\cdot)^\star$ is used to  evaluate a term at the nominal configuration. Note that the surrogate margin $\alpha$ is not invariant to scale, i.e., we could artificially increase the margin simply by scaling up the magnitude of all the forces. To regulate this behavior, in practice, we add bound constraints to the allowable $\Delta\mbf{q}$ and $\mbs{\Delta}\mbf{w}$.
The optimization program in~\eqref{eq:linear_tactile_controller} takes the form of a convex quadratic program under a polyhedral approximation to the friction cone \cite{stewart1996implicit}.

\begin{figure}[t]%
\centering
 \includegraphics[height=3.3cm]{figures/stability_margin.png}
\caption{{\small The stability margin $\phi$ evaluates how close a contact is to the slipping boundary. The goal of the contact state controller is to maximize/minimize the stability margin to encourage/discourage  slippage. }}
 \label{fig:stability_margin}%
\end{figure}

\subsection{Object State Control}
\label{sec:contact_state_control}

The contact state controller in Sec.~\ref{sec:contact_mode_control} regulates the applied forces on the object to enforce a desired contact mode. This tactile controller reacts to fight against external perturbations but does not have the ability to change the planed trajectory of the object in response to a perturbation. To address this, we design an object state controller running in parallel  tasked with replanning object/palm trajectories to drive the object to  its target location.

In this paper we track two types of features: points (corners of the object) and lines (edges of the object).
We formulate the tactile object state estimator as an optimization program that updates the pose ${\mbf{p}}_o$ of the object to satisfy the geometric constraints associated with the tactile features, as shown in Fig.~\ref{fig:state_observer}. We quantify the error between the previous and the updated pose estimate using the distance  $d_{TS}(\mbf{p}_o, \mbf{p}_o^\star)$, where $d_{TS}$ is defined as the weighted sum of the Euclidean
metric in $\mathbb{R}^3$ and the great circle  angle metric in ${SO}(3)$ for the respective components~\cite{holladay2016distance}. We enforce that detected lines are collinear with their associated edge on the object mesh and that the sensed points are coincident with the object corner. In addition to the detected geometric constraint, we constrain the estimated object pose to satisfy the geometric constraints consistent with the current manipulation primitive.
For example, for the \texttt{pull} primitive, we enforce that the bottom surface of the object is in contact and aligned with the table top.

The estimated object pose is used to update the nominal robot palm pose trajectory which allows the robot to adapt to local object perturbations. This is further described in Sec.~\ref{sec:trajectory_planning}.

\begin{figure}[t]
\centering
{
			\includegraphics[width=8.3cm]{figures/object_pose_estimation.png}
}
\caption{{\small \textbf{Tactile object localization}. By localizing descriptive object geometric features (lines, points), we can track the pose of the object.}}
\label{fig:state_observer}
\end{figure}

\begin{figure*}[t]%
\centering
{%
 \includegraphics[width=18cm]{figures/experiments_regulation.png}}
 \label{fig:regulation_experiments
  }
\caption{{\small \textbf{Closed-loop evaluation of the tactile controller}. We consider the task of maintaining the object in a stationary pose under external perturbations. When perturbed, the contact state controller increases the normal force on the object to prevent slippage while the contact state controller replans robot motions to bring the object back to its nominal pose.}}
\label{fig:regulation_experiments}
\end{figure*}

\section{Planning}
\label{sec:planning}

We formulate the manipulation planning problem as a sequencing of manipulation primitives, as described in Sec.~\ref{sec:mechanics}. This section briefly describes an approach for how to 1) sequence the manipulation primitives to achieve the task and 2) plan robot trajectories within a manipulation primitive a to achieve a desired object transformation.

\subsection{Search for Primitive Sequence}
\label{sec:task_planning}
We formulate the search for the primitive sequence as a graph search problem. We adapt the regrasp graphs developed in~\cite{wan2015improving, hou2018fast} to include a broader set of manipulation primitives. The nodes of the manipulation graph represent possible object stable placements  and the edges represent manipulation primitive actions transforming the object from a stable placement to another. We use Dijkstra's algorithm to search for the shortest path within the constructed graph achieving the desired pose to pose reconfiguration. In the experiment in the title figure, the primitive sequence \texttt{pull$\rightarrow$pivot$\rightarrow$push} is found to achieve the desired object transformations. We refer the reader to~\cite{wan2015improving} and the project website \href{https://mcube.mit.edu/research/tactile_dexterity.html}{https://mcube.mit.edu/research/tactile\underbar{ }dexterity.html} for more details on the graph search planning framework.

\subsection{Trajectory Planning}
\label{sec:trajectory_planning}

After the graph search planner has determined a sequence of primitives, we compute  robot/object trajectories independently for each primitive. We rely on primitive specific planners
for which we require that can regenerate trajectories at high frequency to exploit the timely estimated of object pose from the tactile object tracker from  Section~\ref{sec:contact_state_control}.

For \texttt{grasp} and \texttt{pull}, we plan object motions that kinematically stick to the palms. For these, we directly interpolate between the initial and final poses of the object to determine the palm pose trajectory. For \texttt{pivot}, we compute an interpolated object trajectory between the initial and final poses about a specified center of rotation.  Solving~\eqref{eq:static_equilibrium}, we find the palm pose relative to the object that maintains a sticking interaction at all contacts. For \texttt{push}, we use a Dubins' car planner that computes the time-optimal trajectory  connecting  the initial and final object configurations with a single push based on~\cite{zhou2017pushing}.
We consider pushes on all sides of the object and execute the trajectory with the shortest path.

\section{Results}
\label{sec:results}

We validate our approach to \textit{tactile dexterity} on an ABB YuMi dual-arm robot with real experiments visualized in \href{https://youtu.be/f59FoS-hV7c}{https://youtu.be/f59FoS-hV7c} that evaluate the ability of the tactile controller to handle external perturbations on a table top manipulation task.

The title figure shows snapshots of an experiment where the robot manipulates an object from an initial pose  $\mbf{q}_0 = [0.3,\,\,
-0.2,\,\,
 0.07,\,\,
0.38,\,\,
 0.60,\,\,
 0.60,\,\,
0.38]^\trans$ to a target pose $\mbf{q}_f = [ 0.45,\,\,
0.3,\,\,
0.045,\,\,
0.0,\,\,
0.71,\,\,
0.0,\,\,
0.71 ]^\trans$. To achieve the task, the robot follows the  sequence: \texttt{pull} the object to the middle of the table, \texttt{pivot} the object to its target placement, and \texttt{push} it to its target location. The initial \texttt{pull} primitive is necessary to move the object to a location that allows the robot to perform a pivot manoeuvre with well defined inverse kinematics and that avoids collisions with the environment.

In Fig.~\ref{fig:regulation_experiments}, we evaluate the closed-loop performance of the tactile controller in Section~\ref{sec:tactile_control} on individual primitives. We illustrate the regulation task of maintaining an object in a stationary pose for the \texttt{pull} and \texttt{grasp} primitives. The regulation task allows to better visualize the reactive capabilities of the controller without loss of generality. In each experiment, we apply two successive impulsive forces on the object and evaluate  the stabilizing capabilities of the tactile controller.
Figure~\ref{fig:regulation_experiments} plots the mean and standard deviation of the error between the object's desired and measured poses for $5$ consecutive experiments. In both cases, the controller quickly reacts to the disturbance by 1) detecting slippage events at the contact interfaces and 2) tracking the pose of the object using the detected object edge in the tactile signal. First, the applied normal force is increased following \eqref{eq:linear_tactile_controller} in reaction to the detected slippage at the contact interface. Second, the robot replans its trajectory from updates on the object state and this quickly returns to its nominal pose.  For evaluation purposes, we track the ground truth pose of the object using an Apriltag visual marker~\cite{olson2011apriltag}

\section{Discussion}
\label{sec:discussion}

This paper introduces \textit{tactile dexterity}, an approach to dexterous manipulation that exploits tactile sensing for reactive control.
By structuring the manipulation problem as a sequence of manipulation primitives that render interpretable tactile information, we enable tactile object state estimation and tactile object control, yielding robust manipulation behavior.
This requires targeting primitive interactions to those that have simple rigid body mechanics models and efficient planners.
We show the ability of the tactile controllers to modulate contact forces, track the pose of the object, and handle external perturbations by replanning in real-time.

This research relies on a number of important assumptions about the structure of the problem:
\begin{itemize}
    \item Known environment. The manipulation primitives and planning algorithms are tailored to a flat surface environment.
    %
    \item Manipulation primitives. We use human intuition to develop a library of manipulation primitives that are amenable for planning and control (for controllability).
    \item Tactile Pose Estimation. We assume that the pose of the object can be reconstructed from tactile observations (for observability).
\end{itemize}
These assumptions facilitate the development of state estimation, planning, and control frameworks capable of exploiting tactile sensing for dexterous manipulation. They constitute both the limitations, as well as strengths of the approach.

We believe that there are natural extensions to generalize this approach to dexterous manipulation that puts tactile feedback at the center:

\myparagraph{General Manipulation Primitives.}
Developing manipulation primitives with richer contact interactions (contact switches, stick/slip interactions, etc.) will allow more expressive behavior. Due to increased complexity of the mechanics of these primitives, they will likely benefit even more from real-time tactile observations of contact.
This line of research will requires controller architectures that can reason across hybrid contact switches, e.g. developed in \cite{hogan2016feedback, doshi2019hybrid}.

Another interesting possibility is to learn manipulation primitives from experience, and defining a primitive, broadly speaking, as a behavior for which the robot can learn a stable controller. This would potentially reduce the need for human intuition.

\myparagraph{Object tracking.}
A formal approach to characterizing object observability directly from tactile feedback could assist with identifying discriminative features and grasps of the object. Furthermore, combining touch with vision is a natural extension of this research that would allow the robot to recover from larger object disturbances.

\myparagraph{Task-and-Motion Planning}. Dealing with more complex environments and tasks (e.g., pick up the box and put it on a shelf, or inside a closed drawer), would require to integrate the system with a high level task planner that can sequence manipulation primitives, as is commonly done in task and motion planning, while still being mindful of the mechanics of contact \cite{toussaint2018differentiable, holladay2019force}. Ideally, these planners would be reactive and allow the robot to switch primitives in response to unexpected deviations.

{\small

redpajama_set_name: RedPajamaArXiv


text:
Swaziland competed at the 2018 Commonwealth Games in the Gold Coast, Australia from April 4 to April 15, 2018.

Competitors
The following is the list of number of competitors participating at the Games per sport/discipline.

Athletics (track and field)

Men
Track & road events

Women
Track & road events

Field events

Boxing

Swaziland participated with a team of 2 athletes (2 men).

Men

Cycling

Swaziland participated with 3 athletes (3 men).

Road
Men

References

Nations at the 2018 Commonwealth Games
Eswatini at the Commonwealth Games
Com

redpajama_set_name: RedPajamaWikipedia


Black will forever be fashionable. That said, there is no better way to dress up in a tuxedo than with this exquisitely designed diamond-tipped silky black bowtie. You can take the classic tuxedo look up a notch by wearing a trim bowtie such as this to match with your cummerbund.
Bow Tie is great quality, it comes with great presentation.


NOTE: This instruction works on original remotes with an FCC ID: Z5000A or Z5000B.
1. Twist the key in the ignition going to ON position. Don't start the engine.
2. Turn the valet switch/button ON and OFF 3 times. Your LED will flash and siren chirps once.
NOTE: This instruction works on the generic replacement remote with an FCC ID: Z5000E.


FC Barcelona's Sporting Director talks about Pep Guardiola's decision to join Bayern Munich Vilanova: "He's had time to choose and he selected Bayern Munich, a top notch club"
The German Club officially announced this Wednesday that ex-Barça manager Josep Guardiola will be their new head coach through 2016. The news didn't go unnoticed at the Camp Nou. "I'm very pleased for Guardiola. It means that he's doing well and that he's well rested; it means that he's eager to return to the world of football. Tito said this Tuesday that it's good for someone like Guardiola to be involved in football," said AndoniZubizarreta after tonight's match against Málaga.


Alert: Sign on to Disability Groups Letter Opposing Supreme Court Nominee Kavanaugh!
Several weeks ago, we circulated a statement by the Bazelon Center for Mental Health Law detailing numerous reasons why Senate approval of Supreme Court Justice Nominee Brett Kavanaugh would be disastrous for Americans with disabilities. See more at https://www.nyaprs.org/e-news-bulletins/2018/8/10/bazelon-strongly-opposes-kavanaugh-supreme-court-nomination-details-threats-to-people-w-disabilities.
Please either use this link https://docs.google.com/forms/d/e/1FAIpQLSc0ywSiU6MjyqO7RB0d2f_ox3lclvLFJauguOiu9xfT-rwmKg/viewform to the attached letter to contact your Senators today!


The Jacobs went to the gym this morning and to score some 2/1$ avocados at Sprouts. So here I sit, sipping on the last mildly warm drops of my chai tea reflecting on two whole big years of maintaining a blog - however spotty.
I wouldn't have remembered that today was any type of -versary except that it is Groundhog Day. Which for the UDer is a sacred day of dancing and music and ideally sylvan inebriation (i.e. a kegger in the woods). So tonight I will be meeting up with other alumni to enjoy some free beer and toast the alma mater.
Life is very different today than it was two years ago when I started this blog. Two years ago I was pregnant and finishing grad school and we lived in a tiny converted garage that they have sense demolished and replaced with a mega mansion. Two years ago we'd just lost our beehive and were about to lose our primary income. Two years ago we hadn't met our son.
I could list all the ways our lives have changed in the last couple of years for a VERY long time, but it's more difficult to talk about how I have changed. But I know I've changed a lot. Certainly, I'm still the same person generally: the same quirks, the same annoying habits. I still get uncomfortable and frustrated whenever I try to do my hair or paint my nails. I still tackle all types of labor intensive projects - sewing, crocheting, food fermenting, gardening - without ever mastering any of them. I still make all kinds of To Do lists and almost never complete the tasks.
So perhaps I'm pretty much the same person I was two years ago. Except now that I have a little one, I'm looking at the world from an entirely different angle, and with this new perspective I feel changed to the core.
It reminds me of a painting by an artist I once got to hear speak at a Christians in the Visual Arts (CIVA) Conference I attended as a teenager. I went to a talk by the artist Makoto Fujimara about some of his Images of Grace. He showed us a slide of one painting in particular. The painting was huge and a deep deep red. On the left side of the painting was an unassuming vertical gold line that stretched to the top of the canvas. He talked about this painting - this image of grace. In his broken English he communicated that, because of how the painting was installed, as you approach it all you see is the red. But when you arrive at the painting, when you stop and look - he said - you see the gold line. And then that is all you see.
So. Abruptly done with my somber ramblings. Off with you to other blogs to find some chuckles. Here's a pic of Jake copying his big friend on the swing while wearing his "bastetball shoes."
Happy Groundhog Day! Drink a beer like it was my idea!
Becoming a mother changes everything. You are so right. The whole world looks different. Both good and bad! I remember when my oldest was two and my only, and I would take him places that were ordinary to me, and he would see something new and exciting every time. And that made me look at the place with different eyes. Happy Anniversary!
Happy Groundhog, indeed! I celebrated for you with Kathleen out in the park. And I'm looking forward to many more years of blogging.


text:
Cardigan RNLI saves trio after kayaks capsize at Mwnt
CARDIGAN INSHORE LIFEBOAT were paged at 4:04pm on Sunday 7 July following a call from HM Coastguard. On arrival at the scene it became apparent that the three persons involved, two adults and one child had set off from the beach at Mwnt and after rounding a corner, the female in one kayak capsized when the male in the second kayak went to assist her unfortunately he then capsized as well.
Both of the adults then made their way with the child onto rocks nearby where they were able to attract attention and raise the alarm.
The three casualties were rescued from the rocks and taken to safety on the beach at Mwnt where HM Coastguard teams from Cardigan and Moylgrove were waiting to assist them.
This incident once again highlights the need for all persons to be aware of the dangers of the tides and currents and to ensure they carry suitable safety equipment and correctly fitting lifejackets and also carry a means of calling for help and keep it within reach at all times.
Two shouts in 24 hours for New Quay RNLI

redpajama_set_name: RedPajamaCommonCrawl


A delicious combo of salty sweet popcorn and cake!
Pop popcorn per bag instructions, removing any unpopped kernels.
In a separate bowl, melt cashew butter with coconut oil. Stir in vanilla with both extracts.
Spread popcorn on lined baking sheet and cover with butter mixture. Gently mix and add in sprinkles.


The Region of Durham's works department is advising that work started last month on Simcoe Street North is still ongoing, and will likely continue until later this month.
The work, which started on Nov. 21, is seeing a new sanitary sewer, as well as watermain and storm sewer servicing, in order to support new development on Simcoe between Carswell Avenue and Taylorwood Road.
The region says the work is expected to be complete by Dec. 22, but advises that unfavourable weather conditions may influence this schedule.
"The region realizes that the work may be disruptive, and will make every effort to complete the work as quickly and efficiently as possible," a news release announcing the work states.


text:
Q: A varaible independent its local scope I tried to solve the twoSum Problem with primitive tools of car and cdr

Given an array of integers, return indices of the two numbers such
  that they add up to a specific target.
You may assume that each input would have exactly one solution, and
  you may not use the same element twice.
Example:
Given nums = [2, 7, 11, 15], target = 9,
Because nums[0] + nums[1] = 2 + 7 = 9, return [0, 1].

The idea is to take a x from nums, then check if x's complement (target -x) is member of set nums-x
The key logic is
if ((memberp complement (remove-first x nums))
then  (list x complement))

Begin with a helper function try nums
(defun two-sum (nums target)
 (try nums))

The main function:
(defun try (nums)
  (let ((x (car nums))
        (complement (- target x)))
    (cond
     ((null x) '())
     ((memberp complement (remove-first x nums))
      (list x complement))
     (t (try (cdr nums)))
     )))

Then I realize that nums in ((memberp complement (remove-first x nums)) should be stay unchanged and independent from the local scope of let.
How could get such a nums?
memberp and `remove-first'
(defun remove-first (item sequence)
  (filter (lambda (x) (not (= x item)))
          sequence))

(defun filter (predicate sequence)
  (cond ((null sequence) nil)
        ((funcall predicate (car sequence))
         (cons (car sequence)
               (filter predicate
                       (cdr sequence))))
        (t  (filter predicate
                    (cdr sequence)))))

(defun memberp(item x)
  (cond ((null x) 'false)
        ((equal item (car x)) x)
        (t (memq item (cdr x)))))

A: Here is a simple recursive function to compute the indexes:
(defun two-sum (list target &optional (pos 0))
  (if (null (cdr list))
      nil
      (let ((p (my-position (- target (car list)) list)))
        (if p
            (list pos (+ pos p))
            (two-sum (cdr list) target (1+ pos))))))

(defun my-position (element list &optional (pos 0))
  (cond ((null list) nil)
        ((eql element (car list)) pos)
        (t (my-position element (cdr list) (1+ pos)))))

The function is initially called with the list and the target. The parameter pos, which initially is not passed to the function, is assigned automatically to 0, and in the subsequent calls it will be incremented by one, so that it tracks the index of the current element of the list.
The first condition checks if the list has less than two elements: if it is empty (or its cdr is empty) the result is nil since no solution is possibile (note that in Common Lisp (cdr nil) is nil).
Otherwise we compute the position of the "complement" of the number in the rest of the list (note that position is a primitive function, so I called my-position its rewriting). If the element is present, we return both pos and (+ pos p) (since the position found is relative to the current position), otherwise (my-position returns nil when no element is found) we recur on the rest of the list.
Note that with this method there is no need to consider every time all the elements of the list.

redpajama_set_name: RedPajamaStackExchange


M2S exists to help meet the physical, emotional, and spiritual needs of all seafarers, regardless of nationality or religion, who visit the Port of Montreal. Some of the services offered include offering prayer, spiritual counsel, and free Bibles in their own language. The team also advocates on their behalf with the appropriate authorities, helps them keep in touch with their families, and dispenses food, beverages, clothing, toiletries and personal items that are not available on-board.
This ministry is currently in the process of fundraising for a new vehicle to transport seafarers to and from their ships. Learn more!


The Sound Healing Foundation is an umbrella organization that supports many projects related to sound. It is dedicated to furthering the understanding of using sound to heal. We serve no dogma or system and believe that intention, rather than technology, is the soul of transformation. The Sound Healing Foundation is committed to the proposition that sound heals: both people and the Earth. Toward that end we are an educational foundation and we manage several projects throughout the world. We invite your contribution and participation.


text:
Prime Time Gala
South Dakota Fed Cattle Challenge
FED CATTLE CHALLENGE REGISTRATION FOR CLASS OF 2021-2022 IS OPEN.
REGISTER FOR THE FED CATTLE CHALLENGE NOW
The South Dakota Cattlemen's Foundation announced the Fed Cattle Challenge program is open for registration. The Fed Cattle Challenge will provide an opportunity for youth (14 to 18 years old) to learn about the science and economics of finishing cattle by participating in a calf finishing program. Participants will understand the process for finishing cattle through ownership of a percentage of a pen of cattle (roughly equivalent to three head) at a custom feedlot, receive curriculum on twelve topics related to cattle feeding, calculate a closeout and present what they have learned to a panel.
"The Fed Cattle Challenge was an excellent way to enhance my knowledge on the cattle feeding industry. Being able to obtain information from industry experts and use real-world investments will allow me to implement more of these tactics into my own cattle feeding operation in the future," stated Logan Schlim, a finalist in a previous Fed Cattle Challenge class. Peter Rausch, another finalist, said, "The Fed Cattle Challenge has given me a live experience with feeding cattle where there is real risk and reward. This Challenge has given me more in-depth insight into different aspects of the cattle industry. Definitely recommend this to other peers."
Awards for the top three participants will be awarded. First place will receive $1,500, second place will receive $1,000, and third place will receive a $500 award.
Each participant will own a percentage of a pen of cattle approximately equal to three head a pen at Winner Circle Feedyard. The participant will be responsible for a 30% equity down payment and the feedyard will finance the remaining value of the cattle and expenses. Following harvest, proceeds from the cattle will be divided among the owners of the cattle, and the three head of cattle participants own, minus the divided costs. Participants will be given the information and closeout template they need to understand, complete, and present to the panel.
"We are excited to offer this program for its fourth year. Our goal with this program is to help develop the next generation of feedlot owners and operators and the feedback from participants has been overwhelmingly positive that we are accomplishing that goal," said Roxanne Knock, South Dakota Cattlemen's Foundation board member.
About the South Dakota Cattlemen's Foundation:
The South Dakota Cattlemen's Foundation facilitates the generosity of the beef industry to educate and build trust with the state's consumers, ensuring the industry's long-term viability. The development of the Fed Cattle Challenge Program is a part of our mission to support the youth that are sincerely interested in careers in the cattle industry.
DETAILS AND REGISTRATION
In 2016, the South Dakota Cattlemen's Foundation scholarship program was established. This scholarship aims to identify and reward students who are interested in the improvement of beef production and promotion.
Scholarships of $5,000, $4,000, $3,000, $2,000 and $1,000 will be awarded to five students from South Dakota enrolled in any post-secondary institution in South Dakota. All awards will be paid directly to the institution the student is enrolled in.
The South Dakota Cattlemen's Foundation joined forces with Feeding South Dakota in May 2013 to create a signature event, Prime Time Gala & Concert, that raises critical funds for their mission by providing more beef to food insecure families throughout the state. Funds also raised from the Prime Time Gala & Concert support the scholarship program that provides $15,000 in funding to students throughout South Dakota. Since hosting the first event in June 2014, the South Dakota Cattlemen's Foundation has raised over $1,514,469 for Feeding South Dakota. The 9th Annual Prime Time & Concert will be held on Saturday, June 18, 2022.
CALL FOR APPLICANTS: VIEW PRESS RELEASE HERE
For a listing of scholarship eligibility requirements and to submit your application, click here now.
Applications must be received by Saturday, February 19, 2022 at 11:59PM CT.
Brianna Beckler, Harrold
Elle Moon, Creighton
Justic Jessop, Presho
Makayla Nelson, Volga
Tate Risse, Martin
Logan Wolter, Wessington Springs
Tyler Bush, Britton
Jonathan Sumption, Frederick
Cutler Michalski, Willow Lake
Kadon Leddy, Stockholm
Colin Geppert, Kimball
Mariah Kessler, Pierre
Sadie VanderWal, Brentford
Collin VanderWal, Volga
Hannah Noonan, Howard
Shane Mueller – Garretson, SD
Kaley Nolz – Mitchell, SD
Justin Ringkob – Lake City, SD
Ellen Schlechter – Orient, SD
Brianna Buseman – Canistota, SD
Kiera Leddy – Stockholm, SD
Jonathan Linke – Woonsocket, SD
Abby Repenning – Mitchell, SD
Join our mailing list for more information about the South Dakota Cattlemen's Foundation
Ⓒ 2018 SOUTH DAKOTA CATTLEMEN'S FOUNDATION | WEB DESIGN BY FOCUS MARKETING GROUP


text:
var dishes = [];
var bookmarks = [];
    //for current dish
var curr;
var i = 0; //tracker number

$(document).ready(function() {
	console.log('upload.js connected!');

	$.getJSON('../json/explore.json', function(data) {
        dishes = data;
        console.log("getJSON activated!");
         //loading slideshow
        curr = dishes.dishes[i];
        console.log(curr);
        var name = curr.name;
        console.log("name variable testing..." + name);
        var im = "../" + curr.imgURL;
        console.log("Image is..." + im);
        $('#dish_img').attr('src', im);

});

	$('#upBtn').click(function() {
		console.log('Submit Clicked!');
		/*var tags = $('#address').val();
		var array = new Array();
		array = tags.split(',');
		console.log(array);*/

		$.post('/addExplore', {
				'dishName': $('#name').val(),
				'restaurantName': $('#restaurant').val(),
				'address': $('#address').val(),
				'imgURL': 'http://lorempixel.com/400/400/food/'

		}, function (res) {});

	/*console.log(newDish);
	console.log(dishes.dishes.length);
	dishes['dishes'].push(newDish);
	max = dishes.dishes.length;
	console.log(dishes.dishes[max - 1]);*/

	})
})

redpajama_set_name: RedPajamaGithub


Description: I was at someone's home and they were moving. There was a big dog that I put in the car, then I found a big animal carrier and a medium sized dog so I put the medium sized dog in the large carrier and put the cat into the small carrier and took both to the car, put the seats down got them settled. I found a leash and clipped it on the bigger dogs collar. I went in and said the animals were in the car so we could leave now. We left all the other stuff there. Now I'm driving and someone else rented this car and it had no steering wheel, it had a pull out diamond type cushion with two side silver squares that you pushed to go right or left. I forgot to take this harness off and was wearing it when I stopped at a woodblock print studio. Someone mentioned I still had it on and I said, Eh because I didn't care, I didn't like that new way of driving. The studio I was in had a wall of amazing woodcut prints. I decided I needed to take studio classes there. There was also a small rack of exercise clothing and it was very thin width.
Dreamer's Analysis: I need to take classes? Art and yoga.


I'm going to say something here that really is no secret; not many companies are making their marketing work as hard as it might. Yes they understand who they are targeting and have campaigns in place to reach those audiences, but deep down are they really connecting and engaging?
In order to be more effective it helps to ask yourself a few questions and answer them honestly. Much like B2C marketing, B2B marketing is embracing a more engaging communications approach. Our clients are talking about us behind our backs and we need to know that those conversations are positive and reflective of a great brand experience.
2. What would they say about you?
3. What would they say about your competitors?
4. What is remarkable about your business?
5. What do you understand about your customers?
Work with your team to answer these questions. From business development to customer service, from accounts to R&D and operations, and yes, even your customers. Spend time considering these questions together; write them on the office whiteboard and in the company newsletter, or talk them through in the sales meeting. Encourage everyone to answer these 5 questions honestly and embrace potential weaknesses as these are your opportunities. Once you understand this feedback you can improve the way you do business, you can improve customer experiences and you can develop your marketing with stories that underpin the real life experiences that are found by working with you.
In order to really connect and engage- listen, be honest and spend some time navel gazing.
If you find these questions helpful then we have another 20 that will also challenge your marketing and story development so feel free to get in touch and we can chat them through with you.


text:
Honors Day
Saints Connect
Artist-in-Residence, Flagler professor to engage in conversation on race, memory and negotiation of spaces
On Tuesday, Oct. 17, the Crisp-Ellert Art Museum's Artist-in-Residence Jamilah Sabur will discuss with Flagler College's Anthropology Professor Dr. Lori Lee the intricacies of racial inequality, memory and migration, through the relationship of Sabur's artwork and Lee's historical, archaeological research. The conversation will take place in the college's Flagler Room at 7 p.m. An overarching concern within Sabur's work has been how bodies negotiate their way in space, both in interior spaces and exterior spaces. This theme, and how it relates to relevant social, economic, and environmental justice issues, crops up repeatedly in Sabur's sculpture, performance and video works.
"There will be mass migration with the new climate affecting the entire globe and it's hard to imagine a future without reconciling the racial inequalities of the present –- mobility isn't as free and fluid for all," Sabur said. "Encountering Lee's archeological and anthropological work in regards to race as a constructed tool was like finding a missing puzzle piece, as it allowed me to connect threads of exploration I previously made into cognitive science research on human interaction and social cognition."
"I am fascinated with our ability to ignore each other and the collective unwillingness to give over interior space to the habit of 'feeling for' others," she continued. "In an attempt to grapple with the collective immobility towards generating an egalitarian society, I am taken to history."
Sabur is an artist working across various disciplines including performance, video, and installation. Sabur was born in Saint Andrew, Jamaica and received her MFA in Visual Arts from University of California San Diego in 2014 and her BFA from Maryland Institute College of Art in Interdisciplinary Sculpture in 2009. She is interested in embodied cognition, social mimicry, dissonance, ritual and the uncanny. Recent exhibition and screening venues include Dimensions Variable, Miami, Fla.; History Miami; Los Angeles Film Forum; (S8) Mostra de Cinema Periférico, A Coruña,Galicia, Spain; Busan Corner Theater, Busan, South Korea. Sabur lives and works in Miami, Fla.
Lee is an Assistant Professor of Anthropology at Flagler College. She received her bachelor's and master's degrees in Anthropology from the University of Texas at Arlington, and her doctorate from Syracuse University. Lee is an historical archaeologist specializing in African Diaspora archaeology. She has been involved in African diaspora archaeology since 1997. Lee has authored articles on slavery in central Virginia and the Caribbean. Her research interests include race, health practices, the materiality of memory and migration.
The CEAM Artist Residency, in collaboration with Flagler College's Department of Art & Design, is a regular program of artists-in-residence to engage in themes of place-making while collaborating with some aspect of St Augustine's local community, the city's significant and varied roles in American history, or its rich natural environment.
A goal of the residency is to foster diverse perspectives on these aspects of our local community, and artists and scholars in a range of fields are invited who integrate and collaborate between the areas of fine art and broader fields of inquiry, such as curatorial practice, performing arts and creative writing.
The CEAM Artist Residency is supported by a grant from the Dr. JoAnn Crisp-Ellert Fund at The Community Foundation for Northeast Florida.
Flagler's Flagler Room is located in the college's Ponce Hall, located at 74 King St. For further information on the exhibition and related programs, please visit the website at www.flagler.edu/crispellert, or contact Julie Dickover at 904-826-8530 or crispellert@flagler.edu. The museum's hours are Monday through Friday, 10 a.m. to 4 p.m., and Saturday, 12 p.m. to 4 p.m.


When it comes to getting your children to schools safely and on time, you can always trust Kangaroo Coach to give you the very best service. Your children will enjoy our safe and comfortable bus fleet with features seldom seen on school buses including individual reclining seats with seat belts and air conditioning. Our 30 years of transportation experience means you are in good hands..
We offer both afternoon and evening PM service on most routes so your child can participate in athletics or other after-school activities.
Kangaroo Coach is a nationwide, ground transportation service with a network of first-class, affiliates located throughout the United States and Canada. We provide our customers with high quality vehicles and the assurance that they will be greeted by drivers who embody the Kangaroo Coach qualities that we call "The Three Ps" -- PERSONABLE -- PROMPT -- PRESENTABLE.
PERSONABLE, PROMPT, RELIABLE and SAFE for school transportation.
We can handle your Special Events needs.


text:
University Transportation's Role in Restoring the Campus Community
Transit professionals at 5,300+ U.S. universities and colleges are preparing for the return of in-person learning in the fall semester. Many university administrators are relying… Continue Reading > "University Transportation's Role in Restoring the Campus Community"
Electrification Will Not Move Us Far Enough
Electrifying our existing transportation network has clear benefits, ranging from reduced climate impacts to improved public health outcomes especially in urban areas. However, our collective… Continue Reading > "Electrification Will Not Move Us Far Enough"
Addressing the Barriers to the Freedom of Movement for All
Prologue: Yesterday, we launched the first of two special episodes of The Movement Podcast showcasing challenges that people with disabilities face to access mobility and… Continue Reading > "Addressing the Barriers to the Freedom of Movement for All"
How Sharp Financial Acumen Can Loosen the Grip on Local Transit Budgets
The questions circling the future of public transit have put transit professionals in a precarious position. How will you predict the level of service needed… Continue Reading > "How Sharp Financial Acumen Can Loosen the Grip on Local Transit Budgets"
Editorial: Can the American Rescue Plan Save Transit from Itself?
Transit professionals around the country joined in a collective sigh of relief with the passing of the American Rescue Plan Act (ARP), and its $30… Continue Reading > "Editorial: Can the American Rescue Plan Save Transit from Itself?"
Editorial: Fixing Transit Requires Grappling with Racism
The U.S. and Europe have vastly different public transit systems. The accessibility and affordability of Europe's robust public transit system is in contrast to the… Continue Reading > "Editorial: Fixing Transit Requires Grappling with Racism"
Reimagining Public Transportation Safety after COVID-19
As we continue to heal from the pandemic, public transit agencies find themselves at the intersection of efficiency and risk mitigation. A great transit system… Continue Reading > "Reimagining Public Transportation Safety after COVID-19"
Let's Get Smart About Airport Ground Mobility
As airports bounce back from 2020's record-low airline passengers while juggling new operational challenges, as well as passenger and employee expectations for a clean and… Continue Reading > "Let's Get Smart About Airport Ground Mobility"
Finally, It's Infrastructure Week in the U.S.
After four years of talk, Infrastructure Week became a "catch-all term" meant to offer empty promises to the American people. From the beginning of 2017,… Continue Reading > "Finally, It's Infrastructure Week in the U.S."
Accelerating Public Transit's Recovery with Scenario Planning
As we all know, COVID-19 had a dramatic impact on public transit ridership. According to a report prepared for the American Public Transportation Association, public… Continue Reading > "Accelerating Public Transit's Recovery with Scenario Planning"
Health is everything. If that wasn't clear before, then the COVID-19 pandemic made it crystal. The absence of a healthy body and mind robs people… Continue Reading > "Inequitable Public Transit: A Detriment to Community Health"