text:
\section{Experiments}
We report here the results of experiments on pre-grasp sliding manipulation both in simulation and using a real robot and objects to evaluate the proposed approach. A video of the experiments is attached as a supplementary material.

\subsection{Experimental Setup}
The experiments are performed on both simulated and real robot and six objects: a book, a hammer, a box, a wrench, a snack, and a spray gun.
The simulation experiments are performed using the physics engine \textit{Bullet} and models of the robot and objects.
In the real setup, we used a \textit{Kuka} robot with a \textit{Robotiq} 3-finger hand and an RGB-D camera.

\subsection{Compared Methods}
The proposed algorithm is compared against the following methods. \texttt{Planning with Uniform Mass Distribution} plans each object's trajectory assuming it has a uniform mass and friction. Thus, each cuboid in the object has the same mass and coefficient of friction as the other cuboids.
\texttt{Deterministic Planning with Identified Mass Distribution} uses Algorithm~\ref{identificationAlgo} to learn the material properties  from the same data as our method, but samples one model of mass and friction $(\mathcal{M}^{(k)}, \mu^{(k)})\sim P$, and plans the object's entire trajectory based on the sampled model. All the results of this method are averaged over ten samples of $(\mathcal{M}^{(k)}, \mu^{(k)})\sim P$.
Note that each of the sampled models is already locally optimal and yields accurate predictions of the sliding motions.
Because ground-truth mass and friction are known in the simulation experiments, we also compare the proposed algorithm with \texttt{Planning with Ground-truth Mass Distribution} which provides an upper bound.

\subsection{Model Identification Results with Real Robot and Objects}
Figure~\ref{fig:massfric} shows qualitative results obtained from Algorithm~\ref{identificationAlgo} and five pushing actions per object.
Each object is represented by a set of cuboids shown in the first rows. The number of cells per object varies from 28 to 88 depending on the size of the object.
Notice that models of the same object have different mass and friction maps, however, they produce similar frictional forces given by the product of the mass with the friction at each cell.
This ambiguity justifies the need for probabilistic reasoning about the models to avoid pushing the object into unbalanced configurations.

\begin{figure}[h]
    \centering
    \includegraphics[width=0.47\textwidth]{./img/sims_aver.png}
    \caption{The average predicted cell position error (in meters) as a function of the number of training data. The results indicate that active action selection does not offer any clear advantage over a random strategy in this task.}
    \label{fig:res_ntrain}
\end{figure}

\begin{figure}[h]
    \centering
    \includegraphics[width=0.47\textwidth]{./img/sims_ntrain_sims_vs_cell.png}
    \caption{The average predicted cell position error of real objects (in meters) with different numbers of training examples, given the same upper bounds for the mass and friction coefficients as the ones used in~\cite{ChangkyuRSS2020}.}
    \label{fig:res_ntrain_aver}
\end{figure}

\subsection{Balance Prediction with Real Objects}
\label{sec:res_goal_selection}
Figure~\ref{fig:res_goal_selection}  evaluates the accuracy of the proposed algorithm in selecting goal configurations based on the stability of the object.
We manually placed each of the six real objects in $10$ random poses on the edge of a round table where the form-closure grasp of the object is available.
And we recorded whether the objects fell or stayed on the table in each pose.
\texttt{Planning with Uniform Mass Distribution} and \texttt{Deterministic Planning with Identified Mass Distribution} returned any pose that is predicted to be stable in simulation using the identified or provided models.
The proposed algorithm returns any pose where the object is predicted to remain balanced with a probability higher than the threshold of $0.90$.
The reported results are averaged over $100$ independent trials per object. The proposed probabilistic method obtained the highest success rate,
because it considers many possible mass models, with different probabilities, and selects the safest goal poses. The other methods select one mass model per trial for predicting the balance of the object.
Again, note that all the mass and friction models here are obtained from the same identification process, and they are all local minima of the loss function with virtually similar objective values.

\begin{figure}[h]
\includegraphics[width=0.47\textwidth]{./img/planning/stable.png}
\caption{Percentages of stable goal configurations selected by different methods using real robot and objects}
\label{fig:res_goal_selection}
\end{figure}

\subsection{Pre-grasp Sliding Manipulation}
\subsubsection{Simulation Experiments}

Each of the objects is placed in $100$ random rotations at the center of a round table with a radius of $0.5m$. After identifying the models of each object from five pushing actions, the goal is to select
a sequence of pushing actions that displace the object into graspable goal configurations at the edge of the table.
To make the experiments more challenging, we added a control noise to the simulator so that planning does not perfectly match with execution.
Figure~\ref{fig:res_plan_sim} (a) shows that in all of the $100$ trials with random initial object poses, the proposed method successfully placed the object in graspable and balanced poses.
\textit{Planning with Uniform Model Identification} failed mostly because it chose unstable poses as a goal due to the fact that most objects do not have a uniform mass.
\textit{Deterministic Planning with Identified Mass Distribution} also dropped the object when it chose inaccurate mass maps, which results in selecting unstable goal poses.
As shown in Figure~\ref{fig:res_ntrain_aver}, all the identified mass models perfectly predicted the motions of the objects while they were entirely on the surface because  they were used jointly with inaccurate friction maps that compensate for the mass in the product
mass$\times$friction, as shown in Figure~\ref{fig:massfric}.
The proposed method achieves the highest success rate, with a slightly higher number of pushing actions than other methods, as shown in Figure~\ref{fig:res_plan_sim} (b).

\begin{figure}[h]
\centering
\begin{tabular}{c}
\includegraphics[width=0.47\textwidth]{./img/planning/sims_plan_succs.png}\\
{\small (a) Percentage of successful grasps} \\\\
\begin{tabular}{|l|c|}\hline
Method & \# of Actions\\\hline
Planning with Uniform Mass & $6.05~(\pm 1.76)$ \\
Deterministic Planning & $5.69~(\pm 1.96)$ \\
Probabilistic Planning (Proposed)& $6.27~(\pm 1.82)$ \\
Planning with Ground-truth Mass & $5.46~(\pm 1.44)$\\\hline
\end{tabular}
\\\\
{\small (b) Average number of executed actions}\\
\end{tabular}
\caption{Simulation results on pre-grasp sliding manipulation}
\label{fig:res_plan_sim}
\end{figure}

\subsubsection{Real Robot Experiments}

We repeated the same experiment as the previous one using the real robotic setup with a hammer set in $10$ different initial poses on the table.
The \textit{Kuka} robot repeatedly observes the object's pose on the table and pushes the object toward pre-grasp poses, using Algorithm~\ref{algo}.
Once the object reaches a goal pose, the robot grasps the object by inserting its fingers around it and aligning the approaching direction to the surface normal of the contact point.
Figure~\ref{fig:res_plan_real} shows the percentage of successful grasps resulting from the entire process of Algorithm~\ref{algo}.
The lower success rate of \textit{Planning with Uniform Mass Distribution} and \textit{Deterministic Planning with identified Mass Distribution} mostly comes from unstable goal pose selections.
The \textit{Uniform Mass Distribution} shows much lower success rate than simulation results.
This is because reachable object poses with the real robot are more limited than in simulation, and plans obtained from inaccurate mass distributions lead into pushing the object out of the workspace.
The proposed method was able to avoid those situations by considering many plausible models simultaneously and choosing actions that are most guaranteed to succeed.
\begin{figure}[h]
\includegraphics[width=0.47\textwidth]{./img/planning/real_succ_plan_length.png}
\caption{Real robot results on pre-grasp sliding manipulation}
\label{fig:res_plan_real}
\end{figure}
\section{Conclusion}
Manipulating non-uniform objects safely requires accurate models of mass and friction maps. In this work, we proposed a new method for identifying such models automatically from data collected by using a robotic manipulator.
When the data is limited and collected from quasi-static motions, it is not possible to disentangle mass from friction. Therefore, the proposed approach returns a probability distribution over several plausible models. We have also
shown that the inferred probability distribution can be used to plan side-pushing actions that displace an object into the edge of  a support surface while keeping it balanced. The object can then be grasped easily from the edge.
Future work includes investigating other probabilistic mass and friction representations, as well as inferring models of objects in highly cluttered scenes where contact and collisions between different unknown objects may occur.

\section{Introduction}
Pre-grasp sliding manipulation of objects is a useful skill that is necessary when an object is too thin relative to the size of a robotic hand to be directly grasped from a flat surface. This skill is also useful for the execution of tool-use grasps where the orientation of an object needs to be modified before picking up the object. For example, a pre-grasp sliding manipulation approach that was recently developed~\cite{Kaiyu2019} consists in performing a sequence of non-prehensile actions such as side-pushing and top-sliding to relocate an object to the edge of a table so that part of the object sticks out of the table. The object could then be grasped by a robotic hand without colliding with the table.

Most existing methods for pre-grasp sliding manipulation require the existence of predefined geometric and mechanical models of the target object~\cite{Dogar2010PushgraspingWD,6631288,Dogar_2012_7076,King2013PregraspMA,Dogar2011AFF,Dogar2012}. Such models are necessary for selecting appropriate pushing forces and ensuring the balance of the object when it is moved to the edge of the support surface. In this work, we relax this assumption and consider the general problem of side-pushing an unknown object toward a stable goal configuration.

The proposed approach consists in first identifying the mass distribution of the target object, and its coefficients of friction with the support surface, from observed motions and rotations of the object. While there are many techniques for identifying mechanical properties of objects from data~\cite{Mason86,doi:10.1177/027836499601500602,23847993b652419a91558fd1f03bbec3,doi:10.1177/027836499601500603,Dogar2010PushgraspingWD,DBLP:conf/icra/ZhouPBM16,DBLP:journals/ijrr/ZhouHM19,DBLP:journals/ijrr/ZhouMPB18,Yoshikawa1991IndentificationOT}, existing works identify only the friction parameters, and assume that the mass distribution is known.
Moreover, several existing methods assume that the bottom surface of the object has a uniform homogenous friction everywhere. In this work, we consider a grid-based representation of the target object. The object is modeled as a large set of small cuboids
that are attached to each other. The mass and coefficient of friction of each cuboid can be different from the others. The analytical gradient of the object's motion is then used to learn the mechanical properties of each cuboid.

\begin{figure}[t]
    \centering
        \includegraphics[width=1\linewidth]{KUKA_with_book.jpeg}
    \caption{Robotic setup used in the experiments. In this scenario, the robot pushes a book into a desired final pose through consecutive pushes. The choice of contact points and pushing directions is non-trivial as it depends on the mass distribution and the friction coefficients of different parts of the object, which are unknown {\it a priori}.}
    \label{fig:robot}
\end{figure}

The problem of identifying the mass distribution and friction coefficients of an unknown object is under-determined. From the small number of data points collected by the robot by pushing the object, there is a large subspace of possible mass and friction values that can be identified and that explain the observed motions equally well. This problem is particularly pronounced in quasi-static motions, where the mass of the object cannot easily be disentangled from its friction with the support surface. To solve this problem, the full spectrum of possible mass and friction values should be retained. Therefore, our method returns a probability distribution of the mass and friction parameters that can be used to deal with this problem of uncertainty. In a nutshell, we perform several gradient-descent searches starting from different random initial points, and retain all the local minima. A soft-max function is then applied on the values of the local minima to obtain a probability distribution on possible mass and friction values.

The obtained distribution is used, in a second phase, for robust probabilistic planning.
Planning here refers to the problem of finding a stable goal configuration and a sequence of side-pushing actions that displace the object to the goal while preserving its balance. Probabilistic planning consists in searching for actions in a {\it multiverse} simulation~\cite{ISRR2019}, wherein next states are sampled from various models of mass and friction of the object, with different probabilities. The sequence of actions that has the highest success probability is then selected and executed.
The proposed technique for probabilistic model identification and manipulation planning is evaluated both in simulation and on real objects and robot.
The results clearly confirm the advantage of using this technique to safely manipulate unknown objects.

\section{Proposed Approach}
While the exact value of the mass of each cell of the object is relatively irrelevant in quasi-static motions, that is not the case when the object is pushed to the edge of the table and where one needs to predict if it remains stable there or if it drops, which is necessary for pre-grasp sliding manipulation.
In the following we propose a solution to this problem by first presenting a probabilistic model identification approach that returns a distribution on joint mass and frictions maps, instead of single point estimates. We then show that the probabilistic model can be used for robustly selecting stable goal configurations and planning pre-grasp sliding manipulation actions.

\subsection{Probabilistic Mass and Friction Model}
We generalize the deterministic model presented in Section~\ref{sec:setup} into a probabilistic one. The probabilistic model is given by a set of mass and friction maps denoted by $\Theta = \{ (\mathcal M^{(1)}, \mu^{(1)}) , (\mathcal M^{(2)}, \mu^{(2)}) , \dots (\mathcal M^{(K)}, \mu^{(K)}) \}$ and a probability measure $P: \Theta \rightarrow [0,1]$. Each $(\mathcal M^{(k)}, \mu^{(k)})$ corresponds to one of $K$ sampled models, and is defined as explained in Section~\ref{sec:setup}. Thus, $\mathcal M^{(k)}$ is a diagonal matrix, where the diagonal corresponds to the moment of inertia and the mass of each cell of the object, and
$\mu^{(k)}$ is a diagonal matrix, where the diagonal corresponds to the coefficients of friction between each of the $n$ cells of the object and the support surface. We assume that: $\forall k\in \{1,\dots, K\}, \forall i\in \{1,\dots, n\}: \mu^{(k)}_i\in[0,\mu_{max}]$, and $\mathcal M^{(k)}_i\in[0,\mathcal M_{max}]$ where $\mu_{max}$ and $\mathcal M_{max}$ are given upper bounds. We will explain in the following how models in $\Theta$ are sampled and how their probabilities are computed.

\subsection{Inference of Probabilistic Models}
The proposed inference algorithm consists in collecting a real-world trajectory  $ (x^g_{t},\dot{x}^g_{t},F_{t})_{t=0}^{T}$ of ground-truth object poses and velocities that result from a sequence of selected exploratory pushing forces $(F_{t})_{t=0}^{T}$. Different models $(\mathcal{M}^{(k)}, \mu^{(k)})$ of mass and friction distributions are then returned by the algorithm, with different probabilities. Each one of the models is obtained by upper-bounding the mass and the friction with a different limit, and using the gradient-descent method to search for values of mass and friction that minimize the simulation-to-reality gap.

The main steps of this search process are explained in Algorithm~\ref{identificationAlgo}.
The set $\Theta$ of sampled models is initially empty. The first pushing action is selected arbitrarily as no prior information about the object's mass or friction distributions is available. The magnitudes of all the pushing actions are small to avoid moving the object out of the robot's workspace. At each iteration of the algorithm, an action is selected by calling the subroutine \texttt{ActionSelection} presented in Algorithm~\ref{actionSelection}, which will be explained in the next subsection. The returned action $F_T$ is applied on the object. The next pose and velocity of the object
$(x^g_{T+1},\dot{x}^g_{T+1})$ is extracted from recorded depth images after removing the background.

The inner loop of the algorithm (lines 7-27) consists in setting different upper bounds for mass and friction and optimizing $(\mathcal{M}^{(k)}, \mu^{(k)})$ to produce simulated trajectories of the pushed object that are as close as possible to the recorded ground-truth.
In the first half of the total number of iterations $K$, the upper bound of the mass per cell is fixed to $\mathcal M_{max}\in \mathbb{R}$ whereas the upper bound of the friction is gradually increased at each iteration. In the second half, the upper bound of the friction is fixed to $\mu_{max}\in \mathbb{R}$ whereas the upper bound of the mass per cell is gradually increased at each iteration. Other combinations of mass and friction upper bounds can also be considered as well. The provided upper limits are the same for every cell in the object, whereas the mass and friction maps learned by the algorithm are highly heterogenous, as will be shown in the experiments.

At each iteration $k$ of the inner loop, the state of the object in simulation $(x_0,\dot{x}_0)$ is reset to the ground-truth initial state $(x^g_0,\dot{x}^g_0)$ (line 13). Both the mass matrix $\mathcal{M}^{(k)}$ and the friction matrix and $\mu^{(k)}$ are initialized to half their maximum values (lines 14-15). In the next steps (lines 17-25), $\mathcal{M}^{(k)}$ and $\mu^{(k)}$ are iteratively used to predict in simulation the trajectory of the manipulated object under the same forces $\{F_t\}_{t=0}^{T}$, and updated so that the predicted pose $x_{t+1}$ is as close as possible to the observed ground-truth $x^g_{t+1}$. The sum of the differences over all time-steps is denoted by $loss^{(k)}$. The mass and friction matrices are updated by using the stochastic gradient of the loss (lines 21-22), wherein $Diag\big(x_{t+1} - x^g_{t+1}\big)$ is a diagonal matrix that contains $\big(x_{t+1} - x^g_{t+1}\big)$ in the main diagonal, in accordance with the definitions of $\mathcal{M}$ and $\mu$ provided in Section~\ref{sec:setup}. The mass and friction are increased or decreased depending on the signs in the error vector $\big(x_{t+1} - x^g_{t+1}\big)$, which corresponds to the reality gap.

Finally, the models identified from the full sequence of pushing actions are weighed based on their respective accuracies, using a soft-max function (line 30). The soft-max operator returns a probability distribution over competing models of mass and friction distributions. Each of these models corresponds to a local minimum of the reality-to-simulation loss, obtained by gradient-descent. Despite the fact that most of these models achieve a very small loss in predicting the motion of the object on a flat surface, they vary significantly in the way they attribute the observed motion to the mass or to the friction coefficients. While this difference is not consequential on a flat surface, it is important for predicting the balance of the object when it is pushed all the way to the edge of the support surface.

\begin{algorithm}[h]
{\small
     \SetAlgoLined
     \KwIn{Learning rate $\alpha_{\textrm{rate}}$; Total number of pushing actions $nb\_actions$; Upper bounds $\mu_{max}$ and $\mathcal M_{max}$\;}
\KwOut{A set $\Theta$ of mass and friction maps, and a corresponding probability distribution $P$\;}

Sample a random contact point from the object's contour, and use the robot's fingertip to push the object from the sampled point with a small random force $F_0$; Record the observed new state $(x^g_1,\dot{x}^g_1)$\;
$\Theta \leftarrow \emptyset$\;
\For{$T = 1,nb\_actions$}
{
    $F_T\leftarrow$ \texttt{ActionSelection}($\Theta, \{F_0,\dots, F_{T-1}\}$)\;
    Apply $F_T$; Record the observed new state $(x^g_{T+1},\dot{x}^g_{T+1})$\;
    $\Theta \leftarrow \emptyset$\;
    \For{$k = 1, K$}
    {
        \eIf{$k \leq \frac{K}{2}$}
        {${\mu}_{max}^{(k)}\leftarrow \frac{2k}{K}{\mu}_{max}$;
        ${\mathcal M}_{max}^{(k)}\leftarrow {\mathcal M}_{max}$\;
        }
        {${\mu}_{max}^{(k)}\leftarrow {\mu}_{max}$;
        ${\mathcal M}_{max}^{(k)}\leftarrow (\frac{2k}{K}-1) {\mathcal M}_{max}$\;
        }
        $(x_0,\dot{x}_0) \leftarrow (x^g_0,\dot{x}^g_0)$; $x_1 \leftarrow x^g_1$\;
        $\mathcal M^{(k)} \leftarrow \frac{1}{2} I{\mathcal M}_{max}^{(k)}$\;
        $\mu^{(k)} \leftarrow \frac{1}{2} I{\mu}_{max}^{(k)}$\;
        $loss^{(k)}\leftarrow 0$\;
        \For{$t = 0, T-2$}
        {
           $\dot{x}_{t+1} \leftarrow V(x_{t},\dot{x}_{t}, F_t, \mathcal M^{(k)}, \mu^{(k)}) $ \Comment*[r]{\tiny \textcolor{blue}{Predicting velocity}}
           $x_{t+2} \leftarrow x_{t+1} + \dot{x}_{t+1}dt$\Comment*[r]{\tiny \textcolor{blue}{Predicting next pose}}
           $loss^{(k)}\leftarrow loss^{(k)} + \| x_{t+2} - x^g_{t+2} \|_2$\;
         $\mu^{(k)} \leftarrow \mu^{(k)} - \alpha_{\textrm{rate}} Diag\big(x_{t+1} - x^g_{t+1}\big) W^t_\mu $\;
          $\mathcal{M}^{(k)} \leftarrow \mathcal{M}^{(k)} - \alpha_{\textrm{rate}} Diag\big(x_{t+1} - x^g_{t+1}\big)W^t_\mathcal{M}$\;
          $\mu^{(k)} \leftarrow \arg\min_{\mu'\in[\mathbf{0}, I \mu_{max}^{(k)}]} \|\mu'-\mu^{(k)}\|_\infty$\;
          $\mathcal{M}^{(k)} \leftarrow \arg\min_{\mathcal{M}'\in]\mathbf{0}, I{\mathcal M}_{max}^{(k)}]} \|\mathcal{M}'-\mathcal{M}^{(k)}\|_\infty$
            \Comment*[r]{\tiny \textcolor{blue}{Projecting the gradients}}
        }
        $\Theta \leftarrow \Theta \cup \{(\mathcal{M}^{(k)}, \mu^{(k)})\}$\;
    }
}
    \For{$k = 1, K$}
    {
    $P(\mathcal{M}^{(k)}, \mu^{(k)}) = \frac{e^{-\frac{1}{\tau} loss^{(k)}}}{\sum_{i=1}^{K} e^{-\frac{1}{\tau} loss^{(i)}}}$\;
    }
\caption{Inference of Probabilistic Mass and Friction Models with Differentiable Physics}
\label{identificationAlgo}
}
\end{algorithm}

\begin{algorithm}[h]
    Create a set $\mathcal S$ of random candidate forces applied on random contact points in the object's contour\;
    \If{\texttt{SelectionMode = random}}
    {$F_T \leftarrow{ \texttt{RandomSample}(\mathcal S) }$ \;}
    \If{\texttt{SelectionMode = mostDifferent}}
    {$F_T \leftarrow{ \arg\max_{F\in \mathcal S}\min_{t\in[0,T-1]}\|F_t-F\|_2 }$ \;}
    \If{\texttt{SelectionMode = mostDistinctive}}
    {
        $D_{max}\leftarrow{0}$\;
        \ForEach{$F\in \mathcal S$}
        {
            $D\leftarrow{0}$\;
            \ForEach{$(\mathcal{M}^{(i)}, \mu^{(i)}), (\mathcal{M}^{(j)}, \mu^{(j)}) \in \Theta$}
            {
                $D\leftarrow D+ \| V(x_{0},\dot{x}_{0}, F, \mathcal M^{(i)}, \mu^{(i)}) $\\$- V(x_{0},\dot{x}_{0}, F, \mathcal M^{(j)}, \mu^{(j)}) \|_2$\;
            }
            \If{$D > D_{max}$}
            {$D_{max}\leftarrow{D}$; $F_T\leftarrow{F}$\;}
        }
    }
    \KwRet{$F_T$}\;
        \caption{\texttt{ActionSelection}($\Theta, \{F_0,\dots, F_{T-1}\}$)}
    \label{actionSelection}
\end{algorithm}

  \begin{algorithm}[h]
{  \small
     \SetAlgoLined
\KwIn{A set $\Theta$ of mass and friction maps, and a corresponding probability distribution $P$\;}
\KwOut{A sequence of pushing forces $(F_t)_{t=0}^{T-1}$ ; }
Find a set $\mathcal X_{waypoint}$ of waypoints by calling $RRT^*(x_0, x_T^d)$\;
$\dot{x}_0 \leftarrow \mathbf{0}; t \leftarrow 0 $\;
\While{$ \mathcal X_{waypoint} \neq \emptyset $}
{
$x_{target} = \arg\min_{x\in \mathcal X_{waypoint}} \|x- x_t\|_2$\;
\Repeat{$\min_{x\in \mathcal X_{waypoint}} \|x- x_t\|_2 \leq \epsilon$}{
   $gap_{min}\leftarrow \|x_0 - x_T^d\|_2$\;
   \Repeat{timeout}{
   {\footnotesize Select cell $i$ from the contour of the object\;}

      {\footnotesize Choose the direction of the force $(f^i_{x},f^i_{y})$ as the opposite of the object' surface normal at cell $i$;}
   $F^i \leftarrow [0,0,0,\dots,f^i_{x},f^i_{y},0,\dots, 0,0,0]$\;
   $gap \leftarrow{0}$\;
   \ForEach{$(\mathcal{M}^{(k)}, \mu^{(k)}) \in \Theta$}
    {
      $gap \leftarrow gap + P(\mathcal M^{(k)}, \mu^{(k)}) \| x_{t} + \dot{x}_{t}dt + V(x_{t},\dot{x}_{t}, F^i, \mathcal M^{(k)}, \mu^{(k)})dt - x_{target} \|_2 $\;
    }
   \If{$gap \leq gap_{min}$}{$gap_{min} \leftarrow gap $; $F_t \leftarrow [0,0,0,\dots,f^{i}_{x},f^{i}_{y},0,\dots, 0,0,0] $\;}

   }
   $\dot{x}_{t+1} \leftarrow V(x_{t},\dot{x}_{t}, F_t, \mathcal M, \mu)$; ${x}_{t+1} \leftarrow {x}_t + {\dot{x}_t}dt$\;
$t\leftarrow t+1$\;
}
{\small
$\mathcal X_{waypoint} \leftarrow \mathcal X_{waypoint} \backslash  \{\arg\min_{x\in \mathcal X_{waypoint}} \|x- x_t\|_2\}$}\;
}
\caption{Robust Planning with Inferred Models}
\label{policyAlgo}
}
\end{algorithm}

\subsection{Active Exploration}
The data used to identify the material properties of the manipulated object is actively collected by the robot. Algorithm~\ref{actionSelection} summarizes three different strategies that can be followed for selecting the pushing forces. First, a finite set $\mathcal S$ of candidate forces is generated by randomly sampling contact points from the object's contour, along with pushing directions and force magnitudes.
The simplest strategy consists in returning a randomly selected force $F_T\in \mathcal S$. Another strategy selects the action that is most different from the previously executed forces $\{F_0,\dots, F_{T-1}\}$, to increase the diversity of the data and to poke different parts of the object. The third strategy consists in simulating the effect of each force $F\in \mathcal S$ on each possible (mass,friction) model from the set of identified models $\Theta$. The action that leads to the highest disagreement between the models is then selected.

\subsection{Robust Planning with Inferred Models}
After a set $\Theta$ of possible (mass, friction) models is identified in the exploration phase by using Algorithm~\ref{identificationAlgo}, Algorithm~\ref{policyAlgo} is called
to plan a new sequence of pushing actions that displace the object into a desired final pose $x^{d}_{T}$. We start by generating a sequence of intermediate poses, i.e., {\it waypoints}, by using the rapidly-exploring random tree ($RRT^*$) algorithm.
Our algorithm then generates a sequence of pushing forces $(F_t)_{t=0}^{T-1}$ that track the waypoints. At each step in the sequence, a force $F_t$ is selected and optimized so that, when executed, the resulting pose of the object is as close as possible to the nearest waypoint. There are several heuristics that can be used to select a force $F_t$ that moves the object to the nearest waypoint $x_{target}$ at time-step $t$. In this work, we use the following strategy. We start by computing the expected center of mass of the object as,
$\hat{x} = \sum_{k=1}^{K} P(\mathcal{M}^{(k)}, \mu^{(k)}) \frac{\sum_{i\in \{1,\dots,n\}}  (\mathcal M^{(k)}_i) (p^i_{x,t},p^i_{y,t})}{\sum_{i\in \{1,\dots,n\}} \mathcal M^{(k)}_i}$
We then find a cell $i$ from the object's outer envelope as
$i = \arg\max_{i\in \{1,\dots,n\}} \frac{ \big(\hat{x} - x_{target}\big)\big(  (p^i_{x,t},p^i_{y,t}) - \hat{x} \big)^T }{\| \hat{x} - x_{target}  \|_2   \|(p^i_{x,t},p^i_{y,t}) - \hat{x} \|_2 } $. In other terms, $i$ is the outer cell that is most aligned to the axis $\hat{x} - x_{target}$. The direction of the pushing force is always selected as the opposite of the surface normal at the contact point. The next step consists in simulating the pushing force and predicting the next state of the object (line 12) according to each (mass, friction) model in $\Theta$. The expected gap (or cost) of the simulated action is the weighted average of the distances between predicted poses, according to different models, and the target pose $x_{target}$, wherein the weights are the probabilities of the various models. This process is repeated by perturbing the location of the contact point, and retaining at the end the one that yields the minimum gap from the target pose.
\subsection{Application: Pre-Grasp Sliding Manipulation}
The proposed probabilistic model identification technique is used in Algorithm~\ref{algo} to perform pre-grasp sliding manipulation of unknown objects. The algorithm integrates the previous ones to solve the problem of pushing an unknown object to the edge of a table and grasping it safely from there. We assume that the goal region is known. First, we start by using Algorithm~\ref{identificationAlgo} to explore the object and identify a set $\Theta$ of possible (mass, friction) models and their probabilities $P$. We then search for a configuration in the goal region that satisfies two criteria: 1) the object can be grasped without colliding with the support surface, and 2) the object is stable with a sufficiently high probability. To this end, a large number of object poses in the goal region are sampled and verified. Sampled poses that do not allow for a collision-free form-closure grasp are eliminated. Also, for each sampled pose, we use the identified mass models to predict in simulation the stability of the object. The failure probability is computed by adding together the probabilities of all mass models where the object is predict to fall from the table. Only goal configurations that ensure the stability of the object with a probability higher than $1-\epsilon$ are selected. The robust planning algorithm~\ref{policyAlgo} is then used to select a sequence of pushing actions to displace the object to the selected goal configuration.

  \begin{algorithm}[h]
  {  \small
     \SetAlgoLined
\KwIn{Point cloud of an object's upper surface; Maximum mass and friction $m_{max},u_{max}\in \mathbb{R}$; Desired goal region $\mathcal G$; Failure probability threshold $\epsilon$\;}
Create a 3D shape $\mathcal S$ of the object by projecting its upper surface down on the support surface\;
Decompose the 3D shape into a grid of $n$ small cuboids\;
Let $x^g_0$ be the vector of ground-truth positions and rotations of the $n$ cuboids at time $0$; $\dot{x}^g_0 = \mathbf{0}$\;
Use Algorithm~\ref{identificationAlgo} to manipulate the object and identify  a set $\Theta$ of possible joint mass and friction models and their corresponding probabilities $P$\;
Sample a goal configuration ${x}^d \in \mathcal G$ where 1) a form-closure grasp of the object is available, and 2) the probability
that the object remains stable under gravity, according to the identified mass distribution, is higher than a threshold $1-\epsilon$\;
Use Algorithm~\ref{policyAlgo} to obtain a new sequence of forces $(F_{t})_{t=0}^{T_{\textrm{\tiny execution}}}$ to push the object to ${x}^d$\;
Execute the sequence $(F_{t})_{t=0}^{T_{\textrm{\tiny execution}}}$ with the robotic hand\;
Apply a form-closure grasp to pick up the object\;
}
\caption{Pre-Grasp Sliding Manipulation of Unknown Objects}
\label{algo}
\end{algorithm}

\section{Related Work}
\begin{comment}
Algorithms for \emph{Model-based reinforcement learning} learn the unknown dynamics of a system from scratch and search for an optimal policy accordingly~\cite{Dogar_2012_7076,LunchMason1996,isbell:physics:2014,ZhouPBM16,abbeel2006using}.
These algorithms often utilize off-the-shelf statistical learning tools, such as a {\it Gaussian Processes} (GP)~\cite{bauza*2018corl,Deisenroth:2011fu,Calandra2016,MarcoBHS0ST17,bansal2017goal,pautrat2017bayesian}, or neural networks~\cite{OhGLLS15,chiappa2017recurrent,finn2016deep,Finn2016DeepSA} to capture the unknown dynamics. Model-based methods were recently employed to collect images of pushed objects and learn models of their motions~\cite{yu2016iros,bauza*2018corl,doshi2020icra,bauza2019iros}. Our approach belongs to the category of model-based RL, but it explicitly uses dynamics equations derived from first principles, which drastically improves its data-efficiency. Identified mass distributions can also be used to predict the balance and stability of the manipulated object in new configurations that are completely different from the ones covered in the training data. For instance, a GP or a neural net typically cannot predict if an object remains stable when pushed to the edge of a table, unless such an example is included in the training data. Examples of unstable configurations are costly because the robot may drop the object and lose it, and require the assistance of a human to reset the object on the support surface.
\emph{End-to-end learning} is an increasingly popular framework
~\cite{levine2016end,agrawal2016learning,FragkiadakiALM15,WuYLFT15,byravan2017se3,finn2016deep},
that is based on learning a direct mapping of images to controls. While this promising approach is flexible, it also requires large numbers of physical experiments, including experiments that result in losing objects and require human assistance.

{\it Black-box, derivative-free}, Covariance Matrix Adaptation algorithm (\texttt{CMA})~\cite{hansen2006cma} was also used to learn dynamical models~\cite{chatzilygeroudis:hal-01576683}. Other black-box techniques, such as \emph{Bayesian optimization} (\texttt{BO})~\cite{shahriari2016taking}, build a probabilistic model of an objective function that does not have a known closed form. A GP is often used to model the unknown function. These techniques generally suffer from a high computational complexity.
Bayesian optimization has been frequently used in robotics for direct policy optimization ~\cite{pautrat2017bayesian,MarcoBHS0ST17,bansal2017goal,Calandra2016}. Bayesian optimization was also used to learn frictions of simple tabletop objects from their observed motions~\cite{Shaojune2018IJCAI}.

\emph{Classical system identification} methods find a dynamics model by
minimizing the difference between output signals in simulation and real-world response data for the same input control signals~\cite{swevers1997optimal,Ljung:1999:SIT:293154}, by using the least-squares method for example. Rigid body dynamical models were also combined with non-parametric methods, such as GPs, for approximating the inverse dynamics of a robot~\cite{nguyen2010using}.
\end{comment}
Past works have explored the {\it mechanics of pushing}~\cite{Mason86,doi:10.1177/027836499601500602,23847993b652419a91558fd1f03bbec3,doi:10.1177/027836499601500603,Dogar2010PushgraspingWD,DBLP:conf/icra/ZhouPBM16,DBLP:journals/ijrr/ZhouHM19,DBLP:journals/ijrr/ZhouMPB18,Yoshikawa1991IndentificationOT}.
For example, Mason~\cite{Mason86} studied the rotation and translation of an object pushed at a single contact point. Lynch and Mason~\cite{doi:10.1177/027836499601500602} also proposed an algorithm for stable pushing with a robotic end-effector. Yoshikawa and Kurisu~\cite{Yoshikawa1991IndentificationOT} showed that a regression method can learn pivot points of a pushed object. A similar setup was considered in~\cite{23847993b652419a91558fd1f03bbec3} with a constraint to ensure positive friction coefficients. The {\it limit surface} is an important concept in these works, it is defined as a convex set of all friction wrenches that can be applied on an object, and it is generally approximated as an ellipsoid~\cite{doi:10.1177/027836499601500603}, or a higher-order polynomial~\cite{DBLP:conf/icra/ZhouPBM16,DBLP:journals/ijrr/ZhouHM19,DBLP:journals/ijrr/ZhouMPB18,Dogar2010PushgraspingWD}.
In contrast to our method, these works identify only the friction parameters, and assume that the mass distribution is known. Moreover, the present work focuses on safe planning when the identified mass and friction models are uncertain due to the small number of actions used to identify the models in a quasi-static regime, in contrast to prior works~\cite{ChangkyuRSS2020,L4DC2020Changkyu,Belbute-Peres2017} that consider deterministic models.

Pre-grasp sliding was also considered in prior works with deterministic pre-defined models~\cite{Kaiyu2019,PackingICRA2019,ChangkyuIROS2019}, black-box model identification~\cite{ShaojunIJCAI2018}, or model-free reinforcement learning~\cite{BoulariasBS15}.

\begin{comment}
Several \emph{natively differentiable physics engines} have been proposed recently~\cite{DegraveHDW16}.
For example, a combination of a learned and a differentiable engine was used to learn to predict motions of planar objects~\cite{DBLP:journals/corr/abs-1710-04102}.
Differentiable physics engines were also used in a planning algorithm to manipulate tools~\cite{18-toussaint-RSS}. A differentiable contact model was used in~\cite{Mordatch:2012} to allow for optimization of several  locomotion and manipulation tasks. A differentiable physical simulation, formulated as a Linear Complementary Problem (LCP), was also recently proposed~\cite{Belbute-Peres2017}.
\end{comment}

\section{Problem Setup and Notations}
\label{sec:setup}
We revisit the problem of pre-grasp sliding manipulation presented in~\cite{Kaiyu2019}. In this problem, the robot's task consists in grasping a rigid object placed on a table. The object cannot be easily grasped, so the robot slides it to the table's edge where part of it sticks out of the table and becomes exposed. The robot can then easily grasp it from there. In this work, we consider the same problem, but for objects with unknown shape and material properties. The problem then consists in 1) selecting pushing actions to explore the object and gather data points about it, 2) inferring the mass and friction distributions of the object from the observed motions, 3) selecting a stable goal configuration $x^d_T$ from which the object can be grasped, and 4) planning a sequence of side-pushing actions that
displace the object from its current pose $x_0$ to the desired final pose $x^d_T$.

 The object is represented as a set of small cuboids. The object is thus divided into a large number of connected cells $1,2,\dots,n$, using a regular grid structure, wherein each cell $i$ has its own local mass and coefficient of friction.
The object's pose $x_t$ at time $t\in [0,T]$ is given as a vector $x_t = [p^1_{x,t},p^1_{y,t},\theta^1_t,\dots, p^n_{x,t},p^n_{y,t},\theta^n_t]^T$, where $(p^i_{x,t},p^i_{y,t})$ is the $i^{th}$ cell's 2D position on the surface, and $\theta^i_t$ is its angle of rotation. The object's generalized velocity at time $t$ is denoted as $\dot{x}_t = [\dot{p}^i_{x,t},\dot{p}^i_{y,t},\dot{\theta}^i_t]_{i=0}^n$.
The object's mass matrix $\mathcal{M}$ is a diagonal $3n\times3n$ matrix, where the diagonal is $[\mathcal{I}_1,\mathcal{M}_1,\mathcal{M}_1,\mathcal{I}_2,\mathcal{M}_2,\mathcal{M}_2,\dots, \mathcal{I}_n,\mathcal{M}_n,\mathcal{M}_n]$, $\mathcal{I}_i$ is the moment of inertia of the $i^{th}$ cell of the object, and $\mathcal{M}_i$ is its mass. $\mathcal{I}_i = \frac{1}{6} \mathcal{M}_i w^2$ where $w$ is the width of a cuboid.
$\mu$ is a $3n\times 3n$ diagonal matrix, where the diagonal is $[\mu_1,\mu_1,\mu_1,\mu_2,\mu_2,\mu_2,\dots,\mu_n,\mu_n,\mu_n]$. $\mu_i$ is the coefficient of friction between the $i^{th}$ cell of the object and the surface.

An external generalized force denoted by $F$ is an $1\times 3n$ vector $[f^1_{x},f^1_{y},\tau^1,\dots, f^n_{x},f^n_{y},\tau^n]^T$, where $[f^i_{x},f^i_{y}]$ and $\tau^n$ are respectively the force and torque applied on cell $i$. External forces are generated from the contact between the object and a fingertip of the robotic hand used to push the object. We assume that at any given time $t$, at most one cell of the object is in contact with the fingertip.

A ground-truth trajectory $\mathcal T^g$ is a state-action sequence $(x^g_0,\dot{x}^g_0,F_{0}, \dots, x^g_{T-1},\dot{x}^g_{T-1}, F_{T-1}, x^g_{T},\dot{x}^g_{T})$, wherein $(x^g_t,\dot{x}^g_{t})$ is the observed pose and velocity of the pushed object, and $F_{t}$ is the external force applied at time $t$, as defined above.
A corresponding simulated trajectory $\mathcal T$ is obtained by starting at the same initial state
$\hat{x}_0$ in the
corresponding real trajectory, i.e., $\hat{x}_0 = x_0$, and applying
the same control sequence $(F_0, F_1, \dots, F_{T-1})$. Thus,
the simulated trajectory $\mathcal T$ results in a state-action sequence $(x^g_0,\dot{x}^g_0,F_{0},x_1,\dot{x}_1,F_{1}, \dots, x_{T-1},\dot{x}_{T-1}, F_{T-1}, x_{T},\dot{x}_{T})$,
where $x_{t+1} = x_{t} + \dot{x}_{t}dt$ is the predicted next pose.
Velocity $\dot{x}_{t}$ is a vector corresponding to translation  and angular velocities in the plane for each of the $n$ cells, it is predicted in simulation as
$\dot{x}_{t+1} = V(x_{t},\dot{x}_{t}, F_t, \mathcal M, \mu)$.
The goal is to identify mass distribution $\mathcal M$ and friction map $\mu$ that result in simulated trajectories that are as close as possible to the real observed ones.  Therefore, the objective is to solve the following optimization problem,
{\small
\begin{eqnarray}
(\mathcal M^*, \mu^*) &=& \arg \min_{\mathcal M, \mu} loss(\mathcal M, \mu),  \label{simulationError} \\
loss(\mathcal M, \mu)\hspace{-0.2cm} &\stackrel{def}{=}& \hspace{-0.2cm}  \sum_{t=0}^{T-2}\|
\big(x^g_{t+1} +  V(x^g_{t},\dot{x}^g_{t}, F_t, \mathcal M, \mu)dt \big) - x^g_{t+2}
\|_2. \nonumber
\end{eqnarray}
}
Since $x_{t}$ is a vector containing all cells' positions, the loss is the sum of distances between each cell's ground-truth pose and its predicted pose, which is equivalent to the average distance (ADD) metric as proposed in~\cite{Hinterstoisser2013}.

\section{Deterministic Mass and Friction Identification}
I
~\cite{ChangkyuRSS2020},
it has been shown that the gradient of the loss function in Equation~\ref{simulationError} with respect to mass parameters $\mathcal M$ and friction map $\mu$ can be computed analytically, and used to search for ground-truth values $(\mathcal M^*, \mu^*)$ by using the gradient-descent method. The algorithm proposed i
~\cite{ChangkyuRSS2020},
consists in initializing $(\mathcal M, \mu)$ with arbitrary positive values, and repeatedly using $(\mathcal M, \mu)$ to generate a simulated trajectory $(x^g_0,\dot{x}^g_0,F_{0},x_1,\dot{x}_1,F_{1}, \dots, x_{T-1},\dot{x}_{T-1}, F_{T-1}, x_{T},\dot{x}_{T})$, which will then be used to update $(\mathcal M, \mu)$ as follows:
\begin{flalign}
&\mu \leftarrow \mu - \alpha_{\textrm{rate}} \sum_{t=0}^{T-2} Diag\big(x_{t+1} - x^g_{t+1}\big)W^t_\mu,\nonumber\\
&\mathcal{M} \leftarrow \mathcal{M} - \alpha_{\textrm{rate}} \sum_{t=0}^{T-2}Diag\big(x_{t+1} - x^g_{t+1}\big)W^t_\mathcal{M},
\label{updateMassFriction}
\end{flalign}
wherein $\alpha_{\textrm{rate}}$ is a predefined step-size, and $W^t_\mu$ and $W^t_\mathcal{M}$ are weighting matrices provided in~\cite{ChangkyuRSS2020} and computed based on the manipulated object's geometry, current position and velocity. While this approach is shown to return an accurate map of the frictional forces on the surface of a non-uniform object, it cannot disentangle the friction from the mass if all the data points correspond to quasi-static motions. In other terms, the product mass$\times$friction of the identified mass and friction of each cell in the object is close to its ground-truth value, and it is accurate enough to predict the translation and rotation of the object under various pushing actions. However, it cannot tell which part of the product mass$\times$friction is mass and which part is friction. For example, if the ground-truth friction force at some cell of the object is $\mathcal M^*_i \times \mu^*_i = 10 g \textrm{ N}$, where $g = 9.8$, then the update equations~\ref{updateMassFriction} can typically identify a value for $\mathcal M_i$ and for $\mu_i$ such that $\mathcal M_i \times \mu_i \approx \mathcal M^*_i \times \mu^*_i = 10 g \textrm{ N}$ and so that the translation and rotation of the object can be predicted accurately in quasi-static motions on a flat surface. But the identified individual values for mass and friction can be very far from the ground-truth values. For example, any pair of values from the set $\{(1kg,98\frac{\textrm{N}}{kg}), (98kg,1\frac{\textrm{N}}{kg}), (2kg,49\frac{\textrm{N}}{kg}), (20kg,4.9\frac{\textrm{N}}{kg}), \dots \}$ can be returned by the deterministic identification algorithm proposed in~\cite{ChangkyuRSS2020}.

redpajama_set_name: RedPajamaArXiv


If a company decides to rely on an expert to create a window to the web he does to have an advertising return and wants to be located more quickly and comfortably than the old systems (e.g. yellow pages) as the potential customer relies increasingly on search engines like Google.
Unfortunately, not all webmasters, designers are often helped by Dreamweaver & Co, are aware of the dynamics necessary to satisfy the SEO company, then forced to turn to experts to optimize the visibility of your site.
The best case is the presence of an SEO expert in every field of web development company that creates an initial guideline and are consulted each year by the client to update the web space under the new rules SEO: remember that the web and its rules vary very quickly, updating the site code and content is essential for lasting results.
The worst case for the company that wants the site is well positioned to find a study of web development even without knowledge in SEO, at which point the company has to go to another studio or find a freelance SEO expert puts his hand the code and possibly a redesign of a project born evil, or to update.
This is clearly not the optimal situation even for the SEO expert who will have to hand code to others, placing emphasis on analyzing the structure of spider traps, check for broken links, robots.txt misconfigured, not having a semantic structure (wireframe) lacking in content and so on.
So let's see recommendations 4 and 4 things to avoid for a web agency that is planning the site of a customer and want to avoid him spending more money (possibly losing the contract when the customer will notice that your web provider is "lame") offering rather than a service with bows and complete 100%.
The site must be planned together with an SEO expert right from the first drafts, by studying the semantics, the positioning of various frames, the right keywords.
The collaboration must be horizontal: the SEO expert must have decision-making power in the team and give advice during the chosen code, create a site with perfect code badly positioned just a waste of time.
The coder needs to communicate at any time of the project on choices, ideas and possible errors with the expert in SEO. It should not be a consultant but a real team member.
Although the company has knowledge of SEO does not mean that I know what an expert. The SEO changes dramatically, just see the news in the social environment. Having a full-time SEO worker who knows the news is essential to have a result: if the web development company is large enough there is definitely need more experts – why pay 10 programmers flash and then have only an expert in optimization and positioning on search engines? And 'counterproductive.
Show the wireframe to SEO expert to work day after day is over or even very serious mistake. If you follow the expert advice to assist the team, may be too late and having to redesign entire pages.
The team should not be afraid to see the creativity stifled by the expert in SEO: working together to find viable solutions, achieving a balance between creativity and accessibility.
Many webmasters think that the SEO expert is only a marginal figure of the team: the result is to underestimate the SEO aspect and get a website invisible to search engines and therefore useless. If the SEO is not in accord with the design ideas should not hide our heads in the sand and ignore it but you have to ask alternative routes.
If your SEO expert is competent and the team did not collaborate in the production process need to be afraid to launch the site. You have to constantly worry about instead of work: often the most common mistake is to start well and then rest on their laurels.
Monday, August 27th, 201810:49 amThis entry was posted on at and is filed under Uncategorized. You can follow any responses to this entry through the RSS 2.0 feed. Both comments and pings are currently closed.


text:
Home > News & Events > News > Students receive fellowships to address diabetes, cancer, and maternal mortality
Students receive fellowships to address diabetes, cancer, and maternal mortality
Nina Welding • DATE: July 19, 2019
Three students from the University of Notre Dame have received fellowships through the Advanced Diagnostics & Therapeutics' Berry Family Foundation Graduate Fellowship Program. Since 2013, the program has sponsored young researchers during a key part of their education and acted as a springboard for future career opportunities in academic and corporate research.
"Advanced Diagnostics & Therapeutics is proud to have top caliber students be part of the Berry Family Foundation Fellowship Program," said Paul W. Bohn, Arthur J. Schmitt Professor of Engineering and Director of Advanced Diagnostics and Therapeutics. "And in terms of cancer research in particular, we are very happy again to have Notre Dame's Harper Cancer Researcher Institute as a cosponsor. The work these fellows contribute to Notre Dame's research programs is vital to advancing our understanding of today's most pressing health challenges."
The 2019 Berry Family Foundation Graduate Fellowship recipients are:
● Katie Morris, graduate student in the Department of Biological Sciences, is advised by Kevin Vaughn, associate professor of biological sciences, on her research project to develop a potential new treatment for cancer that puts tumor cells into a deep sleep. Morris is this year's combined Berry Family Foundation-Harper Cancer Research Institute Graduate Fellow and her project will involve a collaboration with Richard Taylor, professor of chemistry and biochemistry, the Warren Center for Drug Discovery at Notre Dame, and the University of Southern California.
● Vincent Paul Schmitz, graduate student in the Department of Chemistry and Biochemistry, is advised by Rebecca Whelan, associate professor of chemistry and biochemistry, on his public health project with application to expectant mothers in low-resource areas. Schmitz's project is a collaboration with Marya Lieberman, professor of chemistry and biochemistry, and a teaching hospital in Kenya.
● Michael VandenBerg, graduate student in the Department of Chemical and Biomolecular Engineering, is advised by Matthew Webber, assistant professor of chemical and biomolecular engineering, on his research to support the development of a new, innovative approach to diabetes care.
The Berry Family Foundation Graduate Fellowships are awarded each year to graduate students for 12 months to investigate issues that include combating disease, promoting health, and safeguarding the environment.
For more information, including how to apply, please visit https://advanceddiagnostics.nd.edu/opportunities/berry-family-foundation-graduate-fellowships/.
Advanced Diagnostics and Therapeutics (AD&T) is a research initiative dedicated to combating disease, promoting health, and safeguarding the environment. The initiative fosters cross-cutting exploration and integration across multiple disease areas and health challenges. By fostering a community of researchers spanning disciplines at the University of Notre Dame, AD&T aims to accelerate the research, identify and develop bold new ideas with real-world impact, and support the training of young researchers. To learn more, please visit advanceddiagnostics.nd.edu.
The Harper Cancer Research Institute is dedicated to supporting innovative and integrative research that confronts the complex challenges of cancer. Researchers at the University of Notre Dame and Indiana University School of Medicine-South Bend are united in multi-disciplinary teams with a common goal: to increase the survival of all patients diagnosed with cancer. To learn more about the institute, please visit harpercancer.nd.edu.
— Brandi Wampler, ND Resesarch
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Michael VandenBerg
VIEW FULL PROFILE Email: mvandenberg@nd.edu
Department of Chemical and Biomolecular Engineering graduate student
Click for more information about Michael
Matthew Webber
VIEW FULL PROFILE Email: mwebber@nd.edu
Website: http://www.webberlab.com/
Office: 205B McCourtney Hall
Department of Chemical and Biomolecular Engineering Assistant Professor
College of Engineering Assistant Professor
Research interests: • Supramolecular Biomaterials: The use of non-covalent molecular recognition motifs to engineer highly tunable, dynamic, modular, and bioactive materials for biomedical and biological applications (see Webber et al. Nature Materials 2016) • "Smart" Drug Delivery and ...
Click for more information about Matthew
News >More News
Smart breast clips: A new and personalized approach to breast cancer treatment
Following the laws of physics provides new option for deep learning models
>View more News

redpajama_set_name: RedPajamaCommonCrawl


Energetic brand that implies "Speed, Agility, Power and Faster Services." Perfect fit for Technology brand, Shipping or courier service, Help-desk support, Travel brand, Food Delivery Service, Jogging app, Taxi service.
a Fitness Business or Brand, a Transportation or logistics business, an Automotive business and more!


Event pages: tribe-events/list/single-event.php was changed – to move the date to before the title (changed style), encapsulate the venue and eliminate some address and meta stuff) to get to a single line list format, as well as significant theme changes, to use the Standard template, not the Tribe one, provide a Key, eliminate the end time of events in most displays etc..
functions.php is needed to implement the child theme.
These are added into pages either from the Insert Media button, but may be either from the NextGen gallery or the general Media one. In general you'll want to add pictures about new events into the NextGen gallery for the year.
Typically PDF files are uploaded into the Media gallery and links inserted into pages using this button.
WordPress automatically generates an RSS feed. This is linked to our Facebook page by using the Zapier service (zapier.com, login=webmaster@tunbridgewellscroquet.org.uk). This is restricted to 100 posts per month at no charge.


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It's well documented how the business model for the music industry has flipped 180 degrees. In the past bands toured to sell albums, now bands record albums to sell tours. The hospitality sector has also undergone its own 180 degree shift. For the past 50 years global hotel brands like Hilton have provided home from home comfort and predictability for global travellers and built their entire business model on their ability to do so. Today world travellers eschew global hotels like Hilton because cookie-cutter familiarity is not to their liking at all. Today we want local experiences, authentic connections, sights, sounds and smells that confirm we're in a different place.
So far there are 3 venues in this mini-chain of magnificently eccentric indie properties – in London, Brighton and Cornwall. The London property has just 10 bedrooms and promises "eclectic luxury with a homely charm. Your home from home in West London". There's a cocktail lounge and restaurant – both of which has its own dedicated website – and the property is already award winning. Owned by Charlotte and Justin Salisbury, Artist Residence is clearly a labour of great love, described by the New York Times reviewer "like arriving at an (eccentric) friend's tasteful home". All of the properties have been decorated by local architects and artists and feature vintage pieces of furniture and unique works of art.
superfluous pomp and awkward hospitality are simply an unnecessary burden for modern travelers. In fact, what they really want is precisely the opposite: a fast and uncomplicated change of locations, an attractive hotel design and a cool, clear head for taking in new impressions.
Check-in at some chain hotels can be burdensome and irritating. You see the check-in person mentally going through the list of "welcome" questions from the SOP manual when all you want is a warm shower and a change of clothes. You grit your teeth and answer as politely as you can, mindful that he's just another hospitality guy, like you, doing his gig. At The Daniel design defines the experience but, in common with other indie hotels, this hotel reaches out into its neighbourhood creating spaces, like the lobby bakery (below), where locals and guests collide. Overnight rates are an affordable €98 per room.
Joke Hotel is exactly what its name implies promising surprises, laughs and lots of fun across the entire guest journey. Part of the Paris based, family owned Astotel Group, Joke Hotel is one of 16 hotels that walk to the beat of a different drum when it comes to hotel keeping and hospitality. They are affordable 3 and 4 star hotels with a strong design ethos and an uncompromising customer service focus – the family has been in the hospitality sector for over 40 years. Speciality Architect Philippe Maidenberg has worked on many of the Astotel properties collaborating with artists to avoid "aesthetical clichés and fads". Another of the Astotel properties, the recently opened 123 Sebastopol takes its theme from the world of cinema. The architect consulted with such giants of the screen as Jean -Paul Belmondo, Danièle Thompson and Ennio Morricone to create this unique property.
… located right next to Amsterdam's De Hallen, one of the most hot and happening places in Amsterdam. And why did we put Cityhub here, you ask? Because we want you to go out and discover the beauty of the city as much as you can. Not just the touristy spots, but genuine Amsterdam as it is happening right here, right now.
The tagline says it all: Your Neighbourhood Lifestyle Hotels. There are currently 27 PentaHotels across Europe and Asia (mainly in Germany and the UK) but they have a growth target of 50 hotels by 2020. All PentaHotels have a PentaLounge which is a fusion of reception / bar / cafe / lobby and is designed as a gathering place to attract locals and guest alike. While these properties have a distinctive local look, Penta is actually a global brand, owned by the mainly luxury-focused Rosewood Hotel Group. Penta is a perfect example of a global brand trying to respond to the zeitgeist by assuming a indie look – much like the way Starbucks is now re-positioning itself globally.
Of course Rosewood Hotels are not the only global brand to play the local card. Starwood is doing it with Aloft, Hilton with Canopy, Marriott with Moxy and so on. In each case they create the brand and then build the hotel. But can you actually create a brand and then build the hotel to its specifications?
Pádraic Gilligan is Managing Partner at SoolNua, a boutique consultancy working with venues, hotels and destinations on marketing, strategy and training for the MICE sector.


text:
Thistle diary : More comment from Westminster by Tam Dalyell
By TAM DALYELL
EACH year, at least 12 000 people are killed by landmines. Large tracts of
the best agricultural land are "anaesthetised" by landmines in countries such as
Afghanistan, Angola and Cambodia. Injuries are legion. Last summer Ian Anderson
reported on research by a team at the University of Auckland that suggested
spotting landmines could get easier and cheaper
(This Week, 2 August 1997, p13).
A cake tin, a microwave oven and an infrared camera were part of the recipe
for the New Zealanders' cheap and reliable detector. The news stirred me to
contact John Reid, the Minister for the Armed Forces, to see what Britain is
doing about this dirty business.
The Ministry of Defence, said Reid, spends some £3 million each year on
research into a variety of antimine technologies. "It reflects the high priority
that the government accords to this important area of research," he said. The
MoD does not have a remit to fund research into purely humanitarian mine
removal, as opposed to operational demining. However, Reid said there can be
considerable crossover between technologies for operational and humanitarian
de-mining, and the MoD's experts aim to monitor promising developments across
the board.
British industries and universities are involved in various aspects of the
work on demining technology, but it is assessed and integrated by the Defence
Research Agency. "A separate focal point for post-conflict mine clearance
research is being established at the agency's facilities at Chertsey, Surrey. We
aim to make the results of the work widely available," added Reid.
I get the feeling that the New Zealanders have a practical way of dealing
with the problem, while Britain is hopping around on the periphery, content to
do research. The brave people who work for the Hazardous Areas Life Support
(alias the HALO Trust)—the British charity specialising in mine
clearance— deserve better.
MANAGING Britain's valuable grouse moors is a headache for landowners and
conservationists alike, as Rob Edwards reported recently
(8 November 1997, p 4).
One dilemma arises because peregrines and especially harriers eat grouse, and
the government is committed to maintaining the protected status of all birds of
prey (raptors).
Lord Sewel, the Minister for Agriculture, Environment and Fisheries at the
Scottish Office, says that raptor populations are still only recovering from the
effects of years of persecution and toxic pesticide accumulation. Their species
are now relatively stable, but the majority of Britain's birds of prey are down
to fewer than 1000 pairs, with some under 100 pairs. While the government
recognises the legitimacy of sporting interests' concerns, it is equally
concerned to maintain the protection appropriate to rare and endangered raptor
species, he said. But now Britain has a raptor working group and a forum of all
relevant interests, so there are bodies investigating the game bird problem from
both sides, said Sewel.
The Raptor Working Group, the minister went on to explain, is currently
investigating the relationship between raptors and game birds. It is analysing
the findings of the Game Conservancy Trust's recent survey on the Lanholm Estate
in the Scottish Borders, which Edwards described, on how best to maintain grouse
and raptors in the same habitat. The group hopes to identify practical solutions
that will benefit both groups of birds on the moors.
AS A RESULT of reforms brought in by the Conservative government, the Defence
Evaluation Research Agency (DERA) has to compete with companies for research
contracts awarded by the Ministry of Defence (MoD). The future of DERA is
worrying many companies that it does business with. George Robertson, the
Defence Secretary, tells me that he has asked for a full assessment of a number
of different options for DERA's future, including privatisation, as part of the
strategic defence review. Other options include maintaining the status quo, with
DERA remaining wholly with the MoD, and linking DERA's laboratories to
universities and other institutions in joint ventures.
There are, of course, many considerations, from cost-effectiveness to how the
government might best encourage the transfer of defence technology into civilian
applications. Naturally whatever option is chosen, the intention is that Britain
should remain in the forefront of developing new military technology.
The sooner people know where they are, the better all round.
Magazine issue 2116 , published 10 January 1998
Antipodes : The good oil gives locust protection for crops


Re: "I'll do it, just watch me!"
May I add two points.
1) Boycotting an ISP in many cases also means giving up cable TV. Dropping an Internet subscription while maintaining a cable subscription is the outcome that many ISPs want, and the reason Net Neutrality has become an issue is that people are dropping cable in favor of the Internet for their video entertainment.
2) The people and companies that would suffer most from an effective boycott are those small players that make the Internet such a useful and Interesting place.


Welcome to the 2018 12th Addis International Documentary Film Festival.After eight years, it really does seem like each year keeps getting better. The goal of the Addis Festival, as it's known in the City, is to raise awareness among the public on international issues, to inform, educate and question what we see. It also aims to facilitate creative, contemplative minds and to assemble filmmakers and movie lovers in a community where they can discuss their interests.
As usual, this year's festival will deliver a hugely diverse array of compelling documentaries, instructive workshops and panels, and other special events. I encourage everyone to try to push you with topics and ideas that may be unfamiliar or uncharacteristic to you. So, if you have some free time, or need a break from the end of the day work load, then come and experience the art of documentary films. Take a chance and see something new. You may be surprised how much there is to learn, and how many places there are to see, and new people to meet.
A special thanks to our amazing team, devoted volunteers, great partners, passionate filmmakers, sponsors and to you all for coming to see these amazing films.
Enjoy every second of the Festival, after all it's YOUR Festival!


text:
Q: Validation loss increases after 3 epochs but validation accuracy keeps increasing Training and validation is healthy for 2 epochs but after 2-3 epochs the Val_loss keeps increasing while the Val_acc keeps increasing.
I'm trying to train a CNN model to classify a given review to a single class of 1-5. Therefore, I considered it as a multi-class classification.
I've divided the dataset to 3 sets - 70% training, 20% testing and 10% validation.
Distribution of training data for 5 classes as follows.
1 - 31613, 2 - 32527, 3 - 61044, 4 - 140005, 5 - 173023.
Therefore I've added class weights as follows.
{1: 5.47, 2: 5.32, 3: 2.83, 4: 1.26, 5: 1}
Model structure is as below.
input_layer = Input(shape=(max_length, ), dtype='int32')

embedding = Embedding(vocab_size, 200, input_length=max_length)(input_layer)

channel1 = Conv1D(filters=100, kernel_size=2, padding='valid', activation='relu', strides=1)(embedding)
channel1 = GlobalMaxPooling1D()(channel1)

channel2 = Conv1D(filters=100, kernel_size=3, padding='valid', activation='relu', strides=1)(embedding)
channel2 = GlobalMaxPooling1D()(channel2)

channel3 = Conv1D(filters=100, kernel_size=4, padding='valid', activation='relu', strides=1)(embedding)
channel3 = GlobalMaxPooling1D()(channel3)

merged = concatenate([channel1, channel2, channel3], axis=1)

merged = Dense(256, activation='relu')(merged)
merged = Dropout(0.6)(merged)
merged = Dense(5)(merged)
output = Activation('softmax')(merged)
model = Model(inputs=[input_layer], outputs=[output])

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['categorical_accuracy'])

model.fit(final_X_train, final_Y_train, epochs=5, batch_size=512, validation_data=(final_X_val, final_Y_val), callbacks=callback, class_weight=class_weights)

1/5 - loss: 1.8733 - categorical_accuracy: 0.5892 - val_loss: 0.7749 - val_categorical_accuracy: 0.6558
2/5 - loss: 1.3908 - categorical_accuracy: 0.6917 - val_loss: 0.7421 - val_categorical_accuracy: 0.6784
3/5 - loss: 0.9587 - categorical_accuracy: 0.7734 - val_loss: 0.7595 - val_categorical_accuracy: 0.6947
4/5 - loss: 0.6402 - categorical_accuracy: 0.8370 - val_loss: 0.7921 - val_categorical_accuracy: 0.7216
5/5 - loss: 0.4520 - categorical_accuracy: 0.8814 - val_loss: 0.8556 - val_categorical_accuracy: 0.7331
Final accuracy = 0.7328754744261703
This seems to be an overfitting behavior, but I've tried adding dropout layers which didn't help. I've also tried increasing the data, which made the results even worst.
I'm totally new to deep learning, if anyone has any suggestions to improve, please let me know.

A: val_loss keeps increasing while the Val_acc keeps increasing This is maybe because of the loss function...loss function is being calculated using actual predicted probabilities while accuracy is being calculated using one hot vectors.
Let's take your 4-class example. For one of the review true class is, say 1. The predicted probabilities by the system are [0.25, 0.30, 0.25, 0.2]. According to categorical_accuracy your output is correct i.e [0, 1, 0, 0] but since your probability mass is so distributed...categorical_crossentropy will give a high loss as well.
As for the overfitting problem. I am not really sure why introducing more data is causing problems.
Try increasing the strides.
Don't make the data more imbalanced by adding data to any particular class.

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