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\section{Introduction}
With latest advances in artificial intelligence (AI) it has been shown that the usability of machines is enhanced when they become capable to process data, find patterns and suggest proper actions. From predictive maintenance to improved Human-Machine collaboration, different intelligent systems are currently in use and facilitate our daily tasks. AI based techniques for modeling, forecasting and predicting machine behavior play crucial role in present day Industrial setups. With advancements in industrial connectivity and availability of rich sensory data, AI based techniques can achieve noticeable improvements in both efficiency and flexibility~\cite{fryman2012safety, RSsafe, mandal2019improving}.

Industrial robots are machines that are made to perform complex tasks in factories. A typical robotic application~\cite{gruver1984industrial, lozano1983robot, biggs2003survey} requires a specialist to analytically decompose the complicated task into smaller sub-tasks and actions. The expert expresses detailed instructions in the form of robot programs to make robot accomplish desired goals. This process needs high level of expertise and is time consuming. An alternative approach is to use predefined set of templates~\cite{long2016template} for various tasks, prepared by experts - Block Based programming interface~\cite{weintrop2018evaluating} simplifies the process to some extent without any accuracy loss and makes it easier for non-specialists to learn robot programming. Although these methods help, they do not remove the needs for detailed instructions to be given to the robot every time a new task is to be executed.

\begin{figure}
  \centering
  \includegraphics[width=\linewidth]{imgs/CognitiveBehavior_2.png}
  \caption{Cognition is the ability to acquire and process the information to learn various tasks necessary for living. Part of this information containing rules and facts is stored in Semantic Memory. The model proposed here aims to enhance learning ability of industrial robots. The proposed model is implemented using a dynamic knowledge graphs, where nodes describe the concepts and edges the interactions between the nodes.}
  \label{fig:CognitiveBehavior}
\end{figure}

Considerable effort in research societies and industry is put on making industrial robots more intelligent and thereby more useful. Most of the current work is based on use of neural networks and especially deep learning methodologies. These methods allow the robot learn motor skills in a way close to human learning. This make robots more skillful but for specific purposes~\cite{dnn_robotics} and ~\cite{rl_robotics}. There are other statistical methods that have been used to make robots more capable.~\cite{schaal2002scalable} uses locally weighted learning (LWL) method to learn complex real-time robot tasks. They introduce several ~\textsc{LWL} algorithms to model complex phenomena during autonomous adaptive control of robotic systems.

A useful and smart robot needs to cover large spectrum of applications. As the application areas steadily grow, the systems must accumulate experience, supervise themselves and run diagnosis to avoid mistakes and errors. In an industrial environment, they need to process a work instruction, extract information and execute requested tasks consistent with valid context in the environment, prior collected experiences and rules extracted from product manuals. We claim that reaching a high level of generality, needs more elaborate memory model that are closer to human memory, see figure~\ref{fig:CognitiveBehavior}.

\begin{figure}[h]
 \centering
  \includegraphics[width=\linewidth]{imgs/HumanMemory.png}
  \caption{Generally accepted Human memory structure. Cognitive intelligence for industrial robots can be obtained if a form of declarative memory is conceived that allows the system collect experience, make decisions and use the skills.}
  \label{fig:HumanMemory}
\end{figure}

According to neuro-scientists, human long-term memory can be divided to procedural and declarative. Procedural or implicit memory is the unconscious memory that is used for learning skills, such as balancing, biking and playing musical instruments. These skills are typically acquired through repetition and practice. Declarative memory is where facts and events are stored and can be recalled consciously. Declarative memory is further divided into episodic and semantic memory, figure~\ref{fig:HumanMemory}. Episodic memory stores information about events, while semantic memory accumulates facts and data together with logical relations~\cite{h-mem}. The purpose of the work reported here is to build a model for semantic memory that allows the robot to relate current observations to prior ones, draw conclusion and take actions. Such a memory system is similar to an expert system, that could interact with all sensors and actuators and also dynamically expand.

Regardless memorizing and learning capability of the robot, as any other work force in a factory, robots need instructions to operate in correct manner. In order to accomplish the goal, the robot needs to comprehend the instructions and perform complex sequences of sophisticated and accurate manipulation activities. As the instructions are given by a human, the most intuitive interface would be natural languages. However, in normal conversations concepts are vaguely described and statements and commands are often ambiguous and incomplete. If the instructions are expected to be given as natural language, we need to build a system that resolves the ambiguities in the language.

The problem considered is to give a robot the ability to process a work instruction for an operator, extract information and execute the requested tasks. The robot is expected to execute tasks consistent with valid context in the environment, prior experiences, rules extracted from product manuals and the data collected by all interfaces and sensors available to the system. We aim to do so by semantic analysis of the valid context in relation to the prior knowledge and resolve the discovered ambiguities. New and unseen concepts, if any, are to be made clear in conversation with human operators. The newly defined concepts should be accumulated in a semantic memory representation that allows expansion of the capabilities of the robot. We believe that dynamic ontology-based frameworks are strong alternatives to implement such a memory system. We use Natural Language Processing (~\textsc{NLP}), computer vision and knowledge graphs to demonstrate the effectiveness of our approach on ABB's, IRB 14000, YuMi.

It is worth pointing out that our focus is on semantics of robotic manipulation in a manufacturing environment and we do not intend to cover the world knowledge. The system needs to recognize a set of objects and instructions that are related to manipulation of them, limiting the scope of the problem to object-action semantics for manufacturing purposes. The memory model proposed here is not an alternative to other related machine learning methods, but a complementary function to enhance the learning ability.

\section{Related Work}

With affordable computing power, use of AI for industrial applications has become a realistic target and a subject of academic research and industrial development. Robot Programming languages and systems~\cite{gruver1984industrial, connolly2009technology} have been developed to instruct the robot move accurately in a predefined manner or adapt to the environment by reacting to sensory inputs. Commercially available robot systems~\cite{robotics2019yumi, robotics2014technical} provide that with variable burden on the user; the more accurate the robot system needs to be, the more detailed instructions are to be given and programming becomes more cumbersome. Interestingly, in many of such complex cases, a human operator does not even need any instruction. For example, programming a robot to perform cable assembly is a tough engineering work, while instructing an operator can be as easy as making a circuit diagram available. Experienced workers know how to read the diagram and perform the task and inexperienced ones can either learn very fast or simply find out by experimentation in a very short period of time. The question will then be, to what extent AI can reduce the need for detailed programming and allow robots be instructed as human workers ?

Expert systems were perhaps the first form of artificial intelligence used for industrial automation. They are memory based approaches that comprise of knowledge database and an inference engine. Expert systems have been used in past to solve problems like fault detection and fault tolerance, path and trajectory planning, obstacle detection, robot control, path planar optimization for industrial robotics~\cite{meijer1991robot, kishan2012review,kaldestad2012cad}. The function of semantic memory is very similar to an expert systems with extended ability to handle general sensory information and be dynamically expandable.

With availability of big data sets, machine-learning based robotic solutions have gained considerable traction. Supervised learning in vision AI for identification and sorting of objects~\cite{xia2016workpieces, ali2018vision} and vision-based pose estimation~\cite{yoon2003real} are example of use of AI for robot perception. Unsupervised Learning approaches have also been used in industrial settings to augment safe human robot collaborations~\cite{RSsafe, mandal2019improving}. End-to-end systems that learn robotic grasping~\cite{dexnet, dexnet2, dexnet3} can now assist robots to pick up the parts that it has never seen before. These methods were formulated as learning policies for bin picking of multiple objects in robot's field of view. Similar work has been done by using self-supervised learning~\cite{berscheid2020self, berscheid2020self} for precise pick-and-place application without explicit object models, which has drastically reduced the training time. Bayesian or probabilistic models are used for Imitation Learning~\cite{muhlig2009task, chella2006cognitive}, where the robot learns to perform a task from demonstrations.

Although natural language processing is not a new area of science, with advances in machine learning techniques, the concepts have been elaborated fast during the past years. Semantic role labeling,~\textsc{SRL} is a technique of shallow semantic parsing of natural language text to generate predicate-argument structures of sentences. Shallow Semantic structures play a crucial role in natural language understanding as they abstract syntactic and morphological representations of sentences.Predicates generally are verbs and verbal nouns. They define the core meaning of the situation expressed in the text. Arguments on the other hands are phrases that answers questions as `who?', `where?', `when?' etc. They define the essential details to the situation expressed by a predicate. End-to-end deep models~\cite{zhou2015end} for~\textsc{SRL} seem to work well without syntactic inputs. Traditional Long Short Term Memory,~\textsc{LSTM}, based architectures make sequential prediction for arbitrary length sentences. They treat each sentence as sequence of words and recursively process them. Present state of art methods~\cite{he2017deep, palmer2010semantic} use deep attentional neural networks for the task of~\textsc{SRL}. When compared to traditional Recurrent Neural Networks~\textsc{(RNN)}, they draw the global dependencies of the inputs using deep highway bidirectional~\textsc{LSTM}s with constrained decoding.

Probabilistic Action Cores~\textsc{(PRAC)} interpreter~\cite{PRAC1,PRAC2} for natural-language instructions resolves the vagueness and ambiguity of natural language. The system infers missing information pieces to render an instruction executable by a robot. It formulates this as a reasoning problem from the perspective of Bayesian cognition. The system uses Markov logic networks and learns joint probability distribution over semantic network, to generate specific semantic representation and infer missing aspects of action specification. It is connected to a robot planner that executes the computed plans. Robotic agents have plan library that has parametrizable plans for actions. The actions are refined and instantiated according to the instructions generated. Probablistic Graphical models are constructed for each relevant action verbs and the most probable action cores are inferred. The action roles attached to respective action cores are retrieved from~\textsc{PRAC} knowledge base.

Ontology provides a framework for representing knowledge in a specific domain and assists in understanding a specific attribute of the world. Traditionally ontologies have been used to make web intelligent (semantic web). It captures the structure of the world and describes the domain knowledge. There are no strict and correct ways to describe domain and create ontology. As a standard framework people have used Resource Description Framework~\textsc{(RDF)} and Web Ontology Language~\textsc{(OWL)} to create ontologies for high expressiveness~\cite{olszewska2017ontology, paull2012towards}.

\section{Semantic Memory Model}
In this section, we explain the basic ideas of our memory management system and roughly how the memory manager interacts with the devices present in the system to gather relevant information, store/expand the knowledge and execute correct actions according to instructions. Our focus is robotic manipulation in manufacturing environment following the given set of instructions.

\begin{figure}[t]
 \centering
  \includegraphics[width=0.9\linewidth]{imgs/system.png}
  \caption{Output and input devices are presented on left and right side, respectively. The main actuator is the robot, and information is collected by sensors like camera or command interfaces. The semantic memory as a knowledge graph is in presented in the the middle, where  objects are marked with square boxes and "?" before they are recognized and thereafter with circles. (a), (b) and (c) show consecutive stages of knowledge graph explained in this paper.}
 \label{fig:system}
\end{figure}

For a system to be able to interact with the surrounding and manipulate reachable objects, it needs to sense and recognize the objects in the scene. Recognition in practice is a successful match between measured descriptive quantities and preexisting descriptions. Learning can be understood as accumulation of information in an organized manner. The ability to reuse of that in a controllable manner, to achieve desired effects, can be considered as knowledge. We propose a system that that is able to perform these steps and thereby provide necessary ability to industrial robots to become steadily more competent over-time to perform more manufacturing tasks and in a better way.

\begin{figure*}
\centering
  \includegraphics[width=0.9\linewidth]{imgs/SemanticMemory.png}
  \caption{A semantic memory model to store information and use the experiences in a deterministic manner. We observe a subset of active elements of prior knowledge and scene graphs with associated relational links between the them.}
  \label{fig:SemanticMemory}
\end{figure*}

The system consists of three essential components: (1) input devices and sensors (2) output devices and actuators (3) the semantic memory module. Memory module organizes and stores the collected information from input devices. It commands the output devices such that a realistic interaction with the environment can be possible. We assume that there is an input device to receive commands in natural language and that there is a vision system to be able to sense the surrounding. The robot system, with its conventional controller, is in fact a collection of actuators and sensors. However, at this stage, we consider robot as an actuator that is expected to manipulate objects. Further, we focus on memory management and assume that each device is smart enough for the interaction with the semantic memory manager.

The core of our semantic memory manager is a dynamic knowledge graph, where the nodes represent concepts and objects and edges define relations between two nodes or how they interact with each other. The meaning of the edges, or the related action, is defined by another node in the graph. The graph initially has only a minimum number of concepts, such as elementary geometries like sphere, cube and basic relations like `is', `has' and `is instance of'. The graph grows in interaction with devices connected to the system and what they observe or do. Conversation with a human operator will be necessary for defining new concepts in the system.

The knowledge graph has two distinct parts that are tightly interconnected. These are the ~\textit{scene graph} and ~\textit{prior knowledge graph}. The scene graph describes the surrounding world, while the prior knowledge graph stores all concepts that have been defined earlier. One can argue that the prior knowledge is type definitions while the scene graph holds information of objects that are instances of those types.

Once an object is identified in the scene by one of input devices, its properties are measured and stored. Assuming that the input device is a camera, once an object is detected, the camera is expected to output at least approximations for location of the object, its shape, size and color, so that the object can be identified. This means that the camera is smart enough for our purpose. The measured properties are compared with the existing types and concepts. If a match is found, the identified object is labeled by creating relations to relevant nodes of the prior knowledge graph with information about the type of the object. If no match is found, a new concept will be defined and will be labeled by requiring information from the operator. Obviously, with AI-based image processors, a large number of objects can automatically be labeled, without help from an operator.

\begin{figure}[t]
\centering
  \includegraphics[scale=0.45]{imgs/SystemArchitecture.png}
  \caption{Schematic view of the memory management architecture, interaction between accumulated knowledge and scene view and persistent storage for the prior knowledge.}
  \label{fig:SystemArchitecture}
\end{figure}

As mentioned above, the robot, say ABB's IRB 14000, also called Yumi, is an actuator that operates on operands according to the instructions. Instructions are treated in exactly same manner as an observed object, as explained above. The sentence is recorded by the input device and is analyzed. Once the roles in the sentence are defined, the prior knowledge graph will be searched to find the relevant action or start to record a new one. Execution of instructions means that the robot operates on an object. For the topic of interest, it is not important how the operations are implemented or learnt. They can be programmed in traditional manner, be implemented with some kind of learning method or can be taught the first time the instruction is given and are in general dependent on the type of the operand. A robot system with such an ability is smart enough for our purpose.

Figure~\ref{fig:system} presents an example of the knowledge graph, the system and how the instruction are received. At starting point, the prior knowledge graph contains a number concepts. The vision sensor records the scene and for each object defines its signature so that the corresponding type can be found and associated to the object. These associations are shown as links between elements in the scene graph and elements in the prior knowledge graph.

Figure~\ref{fig:SemanticMemory} shows a subset of the prior knowledge graph related to the actions. Here, we see that three different `Pick' actions are defined for three different objects. At this moment, if the instruction will be `Yumi, pick the nut!' the system will choose pick1, and look for a nut in the scene graph, get its locations and execute the action with those inputs. If a new object will need to be picked, Yumi will be able to suggest to take it as a nut, screw or a clip and therefore no new programming or teaching will be needed if the existing actions are sufficient. The instructions can be more specific with for example specifying the color of the nut. In that case, a nut with the specific color will be sought in the scene graph.
The strength of semantic modeling can be observed here. The system does not learn an instruction as a whole, but learns each concepts separately and combine them to take the right action. How exactly it is done will become more evident by description of the implementation details and analysis of the experiments in the two following sections.

\section{Implementation}
In this section, we present the details of our current implementation. We are building a semantic memory and intend to use it as a knowledge base for robotic manipulation. The complete cycle includes data collection from environment and its analysis, storage in the graphs and its use for execution of instructions, figure~\ref{fig:SystemArchitecture}

\subsection{Semantic Data Processing}\label{section:vision}
\begin{figure}[b]
  \centering
  \includegraphics[width=\linewidth]{imgs/6DoF.png}
  \caption{Workflow for estimation of object coordinates (x,y,z) and object orientation (yaw,pitch, roll).}
  \label{fig:6DoF}
\end{figure}

For a robot to execute a requested instruction and operate on objects, it needs to analyze the inputs from all sensors together with the instruction itself. We consider all input devices together and intend to provide a semantic analysis that allows finding correct objects and actions.

Identification of object, finding its localization, orientation, color, distance from the image sensor etc. are some pieces of information that are needed for proper operation. We use state-of-the-art, real-time object detection~\cite{huang2018yolo} based approach followed by 3-D template matching to detect object coordinates (x,y,z) and compute object orientation (yaw,pitch, roll), see figure~\ref{fig:6DoF}. The system  detects objects in semi-moderate complex environment, viz. homogeneous background with modest illumination variations. The  approach is invariant to shape, size, texture and other properties of objects.

We use NVIDIA® Jetson Rudi Embedded System board~\cite{mittal2019survey} with 1 TFLOP/s, 256 CUDA cores, 8GB LPDDR4 memory, 64-bit ARM® A57 CPUs (6 Core), 32GB eMMC for object pose detection.At heart of $3D$ object recognition and pose estimation is identifying set of correspondences between two different clouds, one of them containing the actual object and other containing the object from actual scene. We use global descriptors to understand the notion of object. The segmented point cloud from detected objects help us identify prospective point cloud clusters. We remove false positives by discarding point clouds smaller than certain threshold - the threshold is computed by observing size of objects in the database. We use, Clustered Viewpoint Feature Histogram,~\textsc{CVFH}~\cite{aldoma2011cad} based global descriptor to compute point cloud descriptors. Once all the descriptors have been computed nearest neighbors search is performed in the database to do correspondance matching, figure~\ref{fig:6DoF}. Viewpoint information encoded in descriptor is used to compute rotation and the roll angle is computed using, Camera Roll Histogram,~\textsc{CRH}. The final angle is computed by aligning the current~\textsc{CRH} with the stored descriptor.~\textsc{CVFH} is the fastest of all global descriptor among all the investigated descriptors. It takes 5ms to compute~\textsc{CVFH} descriptor without down-sampling the point cloud. With down sampling, the descriptor extracting time is decreased to 1ms.~\cite{pcl, pcltutorial}.
\begin{figure*}[h]
\centering
  \includegraphics[width=0.95\linewidth]{imgs/NLP_mini_3.png}
  \caption{Comparison between different methods for understanding instructions using Heuristic over Part-Of-Speech (POS) Taggers, Deep SRL based method and subject-predicate-object triplet extraction.}
  \label{fig:nlp}
\end{figure*}

To process the instructions, we use deep~\textsc{SRL}~\cite{he2017deep} based approach to extract intent from user commands.~\textsc{SRL} determines the latent predicate argument structure of a sentence and provides representations that identify the meaning of sentence and relationships among the actors and verbs in the sentence. To baseline our approach we use Part-Of-Speech~\textsc{(POS)} Taggers to generates intermediate tags and use heuristics to generate formatted output. Figure~\ref{fig:nlp} compares both these approaches along with subject-predicate-object triplet extraction method~\cite{triplet}. Tuning such systems could be difficult and would require considerable efforts to cover all possible use cases. The output of intent extraction step is used by `dynamic' robot world knowledge ontology.

\subsection{Building knowledge graphs}
The knowledge graph was implemented using graph-tool \cite{pygraph}, a Python 3 module for manipulation and statistical analysis of graphs or networks, which supports visualization of graph along with efficient on-the-fly filtering of vertices and edges including standard topological algorithms. Algorithms specific to our application along with interfaces for manipulation of the graphs were added through the API's provided by the library.

As mentioned above, the knowledge graph is divided into two sub-graphs, prior knowledge graph and scene graphs, each of which are populated by nodes interconnected with edges. Each element is an instance of a class and embeds both information and functions. Necessary functionality for information management, retrieval as well as exchange between the two sub-graphs are implemented in the head node of each, while other nodes and edges include graph manipulation operations such as creation, deletion, modification, providing specific instructions to be executed depending on the type of nodes and edges.

Scene graph and prior knowledge are self-contained graphs, but dependent on each other to shape a complete understanding of the information. Initially both are empty, but the prior knowledge can be populated programmatically or by reading from file. The scene graph, which represents current active world, is populated by the sensory data in relation to the prior information available within prior knowledge graph. The steps are presented below.

Nodes in the graph hold two types of information: labels and properties, which either are static or dynamic. Node with static information represent either an object or properties. For example, the color node would store `Red' or `Green' and position node would store the position of object in real world (X=10,Y=55,Z=-10). Dynamic information corresponds to executable code, model or objects representing learned task, figure~\ref{fig:NodesAndEdges}.

The edges are directional and define relation between source and destination nodes. They can either be of predefined type, `has', `is' and `instance-of', or any user defined type, figure~\ref{fig:NodesAndEdges}. The predetermined types of edges have special importance in the graph as follows:
\begin{enumerate}
	\item has: Source node has destination node as a property.
	\item is: Source node is of type destination node or child node of type destination node.
	\item instance-of: Source node in scene graph is an instance of destination node in prior knowledge graph. This is the only relation or edge which spans across both the graphs.
\end{enumerate}

The user defined edges depict task with the given label. For example, `pick', `place', `test' are relations between the robot and an object and point to nodes containing the actual task implementation. The process to add an object to the scene graph with reference available in the prior knowledge graph:

\begin{enumerate}
	\item Object type in scene is detected from sensory data along with its properties such as color, shape, size.
	\item Node matching the object is searched in the prior knowledge graph.
	\item A node is added to the scene graph and is linked with "instance-of" edge with selected Node.
	\item The logic embedded within `instance-of' edge renames the node to `Label\_instance\_number', where `Label' is the type of the object found and the `number' is the instance number. Scene graph does bookkeeping of number of instances of each Node.
	\item A list of all nodes linked to the found node in the prior knowledge graph with "has" edge is created.
	\item Each node in the list is added to the scene graph and attached with the similar structure as in prior knowledge, creating an exact copy in scene graph. This and all previous steps are repeated for all nodes being added to scene graph. Thus, all nodes in the scene graph will be linked to their type nodes in prior knowledge graph.
	\item Properties values collected from sensory data are added to the respective nodes in the scene graph.
\end{enumerate}
\begin{figure}[b]
\centering
  \includegraphics[width=0.99\linewidth]{imgs/NodesAndEdges.png}
  \caption{A general description of nodes and edges in semantic memory design. Nodes with static information represent either an object or properties. Edges define relation between source and destination nodes. 'R','G' and 'B' in graph denote red, green and blue colors respectively.}
  \label{fig:NodesAndEdges}
\end{figure}
If no prior reference is found, additional information will be required to create relations to existing information or expand the prior knowledge.

\subsection{Execution of instructions}
When an instruction is given, it is broken down into parts with roles identified and is expected to be executed in context of current environment with the available accumulated knowledge. To do that, nodes within scene graph and the prior knowledge graph are matched with respective parts in the instruction. This is done in the following way:
\begin{enumerate}
	\item For each named object, e.g. `Yumi' and `nut', the respective node in the prior knowledge graph is found.
	\item For each named object, a list of all nodes connected to the respective node in prior knowledge graph with ``is'' and ``instance-of'' is created.
	\item If there are any properties of the object requested in the instruction such as color or size, the list will be filtered accordingly.
	\item When a set of objects are now identified, the right action with the given label is searched in prior knowledge graph as an edge to link the right actor to the right object.
	\item With all elements in the instructions identified, the instruction can be executed.
\end{enumerate}

The above steps will become clearer with an example. Assume the instruction ``YuMi, pick the nut!''. This is first broken into `YuMi', `pick' and `the nut' and respective nodes in the knowledge graph are identified. Thereafter, the objects in the scene graph that relate to right types are identified; this will result in `YuMi-1' for `YuMi' and `Nut-1' and `Nut-2' for `Nut'. The list will be filtered if for example a specific color is requested. If there is a match the target is found, otherwise the closest match is proposed. For example, if Nut-1 had property color as blue, while green is requested, the system will inform the user about inability to find exact object and will ask whether the blue one can be accepted. Equally, for the action, an edge with label `pick' connecting `YuMi' to a "nut" is searched. If it is found, the action can be executed and if there is none, the closest one will be suggested.

\begin{figure}[t]
 \centering
  \includegraphics[width=0.8\linewidth]{imgs/ExperiementalSetup.png}
  \caption{The experimental setup includes: 1) YuMi IRB 14000, 2) Computing device, a laptop, 3) a Vision sensor and 4) Industrial objects located on the table.}
  \label{fig:ExperiementalSetup}
\end{figure}

The above steps show how information is divided into knowledge that needs to be saved and scene information that should be built dynamically, how it is stored and how the information is used to execute an instruction. The system is complete if the necessary information is available. Further, it was explained how object information is stored in the scene graph and how a new type can be added to the knowledge graph. What remains is the definition of action. In general, we consider the action as a skill that the system should be capable of. Exactly how the skill is learned or programmed is not information that needs to be stored in the semantic memory and therefore not in the scope of this work. In the current system, we have used available interfaces in ABB's controller for a convenient programming. The robot is moved from one position to another, simply by guiding it by hand and the gripper is activated by a command. The actions can of course be provided as a library of pre-programmed skills or as an AI-based skill package that should be trained.

\section{Experiments}

\begin{figure}[b]
 \centering
  \includegraphics[width=\linewidth]{imgs/Experiment1.png}
  \caption{Interaction with system and execution of instruction. When the system starts, prior knowledge is loaded and the scene graph is populated with observed objects (left). The system acts as shown in the middle column: When an instruction arrives, it is analyzed and interpreted with help of prior knowledge (left). The interpretation is matched with the scene graph to find correct objects (middle). Once the objects are identified and proper action is found, the instruction will be executed. (right) The scene graph is updated, reflecting the changes in the environment.}
  \label{fig:Experiment1}
\end{figure}

A typical automation system includes, in addition to the robot itself, a number of other elements. Elements such as grippers, fixtures, sensors, trays etc. work in tandem to make a robotic application come to life. The objective of these experiments is to enable the robot to operate on the repeated tasks with least amount of programming effort. We teach a robot to learn by demonstration and reuse its experiences using the memory structure we propose in this work. Our experimental system, figure~\ref{fig:ExperiementalSetup}, is composed of the following elements:

\begin{enumerate}
	\item YuMi IRB 14000~\cite{robotics2019yumi}, dual arm collaborative robot with grippers .
	\item A table to perform experiments on objects with robot.
	\item Objects such as Nut, Box, Screw etc. kept on the table.
\end{enumerate}

We use vision algorithms, section~\ref{section:vision}, to detect object coordinates (x,y,z) and compute object orientation (yaw,pitch, roll) in semi-moderate complex environment. Intel Core i3 4590 based laptop with $4$GB RAM running on Ubuntu 18.04, was used to interface with Jetson Rudi Embedded System board~\cite{mittal2019survey}. We used Python3 to implement majority of the components of the platform. The robots used were programmed using ABB RAPID~\cite{robotics2014technical} language.

We demonstrate the details of three experiments in the following sections. The details include robot interactions, command execution sequence, status of memory modules and update details of the respective knowledge graphs .

\subsection{Experiment 1:"YuMi, pick the screw!"}
\begin{itemize}
    \item Sensory module detects three items on the table. The item details are added to the scene graph: `nut\_1' with color property `green' of type Nut, `screw\_1' with color property `blue' of type Screw and `box\_1' with color property `gray' of type Box and "yumi\_1" of type robot YuMi.

    \item `yumi\_1' and `screw\_1' are selected and prior knowledge is checked. Prior knowledge has information about `pick' task with respect to YuMi and Screw. Hence, the task is executed and screw is picked and removed, as observed in figure \ref{fig:Experiment1}.

    \item Sequentially, another command "YuMi, pick the green nut!" is given. Both `yumi\_1' `nut\_1' are are found in prior knowledge graph. The task information for ``pick'' with respect to YuMi and Nut and is executed. `nut\_1' is picked and removed from scene.

\end{itemize}
In the above list, \_number denotes the sequence number of objects of the same type. For example, nut\_1 and nut\_2 are the first two instances of object type Nut.

\subsection{ Experiment 2: "YuMi, pick big clip!"}
\begin{itemize}
    \item Sensory module detects two items and adds them to the scene graph : `clip\_1' with color property `green' and shape `big' and `clip\_2' with color property `blue' and shape `small' of type Clip along with `yumi\_1' of type robot YuMi.

    \item As there are two nodes are of type clip, the properties of each node are matched with the shape property `big'. Scene graph has information of `clip\_1' and `yumi\_1' and prior knowledge details of `pick' task with respect to YuMi and Clip. Hence, the task is executed and "clip\_1" is picked and removed.

    \item Sequentially, another command "YuMi, pick the blue clip!" is given, `yumi\_1', `clip\_2' are selected as property `blue' is matched with `clip\_2' and task is executed.

\end{itemize}

\subsection{Experiment 3: "YuMi, pick the new\_obj!"}
Here, "new\_obj" is used to refer to a label that is not known to the system.
\begin{itemize}
    \item Sensory module detects two items and adds them to scene graph : `box-1' with color property `gray' and shape `square' and `new object' with color property `gray' and shape `square' along with `yumi\_1' of type robot YuMi.

    \item The system, informs user about ``new object'' along with detected properties. User instructs the system to add this to prior knowledge graph. User either provides details about its relation to existing objects,i.e "box" or "Nut" etc or instructs the system to create a new type in prior knowledge. In case a new type is created, node `new\_obj\_1' is created in scene graph.

    \item Since details of `pick' are also missing from the graph, system asks user to either train a new task (by demonstration) or link the same to one of the existing task labels based on matching properties of other nodes, figure \ref{fig:Experiment3}.
\end{itemize}
\begin{figure}
 \centering
  \includegraphics[width=\linewidth]{imgs/Experiment3.png}
  \caption{Learning from observations. Initially, the graph includes basic types and relations only (a). When a new object is observed, its features are quantified (b). After that the observation is labeled with human help, both the prior knowledge and scene graph are updated to include the new information (c).}
  \label{fig:Experiment3}
\end{figure}
\section{Conclusion and Future Extensions}

Similar to humans, industrial robots can use a combination of memory models to analyze the environment, register and accumulate the processed information for later use. While general Machine learning based models are good for skill learning, we need semantic memory to learn long term innate concepts. Such memory models are similar to robotic expert systems which are extended to general sensory information. The approach presented in the paper, describes a memory management architecture to learn from complex environments and structure the same for future use. This structure when used in tandem with~\textsc{NLP} engine and vision cues leads to reduction in programming time and increase in productivity.

We intend to extend the capabilities of intent extraction algorithms, so that they can deal with complex set of advanced human commands. This would require improvements in robot execution planners too. Input commands in form of human speech are also being experimented with. To make memory representations more robust and extensible, we foresee a future where base ontology can be initiated using web data and product manuals. The ability to forget experiences over a period of time and to be sure of the ones which robot experiences quite often are few of the memory management features we feel would be important in long run.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


text:
A057526 Number of applications of f to reduce n to 1, where f(k) is the integer among k/2,(k-1)/4, (k+1)/4. 10
0, 1, 1, 2, 1, 2, 2, 3, 2, 2, 2, 3, 2, 3, 3, 4, 3, 3, 2, 3, 2, 3, 3, 4, 3, 3, 3, 4, 3, 4, 4, 5, 4, 4, 3, 4, 3, 3, 3, 4, 3, 3, 3, 4, 3, 4, 4, 5, 4, 4, 3, 4, 3, 4, 4, 5, 4, 4, 4, 5, 4, 5, 5, 6, 5, 5, 4, 5, 4, 4, 4, 5, 4, 4, 3, 4, 3, 4, 4, 5, 4, 4, 3, 4, 3, 4, 4, 5, 4, 4, 4 (list; graph; refs; listen; history; text; internal format)
Also the number of zeros in the symmetric signed digit expansion of n with q=2 (i.e. the representation of n in the (-1,0,1)_2 number system). - Ralf Stephan, Jun 30 2003
Also the degree of the Stern polynomial B[n,t]. The Stern polynomials B[n,t] are defined by B[0,t]=0, B[1,t]=1, B[2n,t]=tB[n,t], B[2n+1,t]=B[n+1,t]+B[n,t] for n>=1 (see S. Klavzar et al. and A125184). - Emeric Deutsch, Dec 04 2006
In this sequence, n occurs exactly 3^n times. - T. D. Noe, Mar 01 2011
G. Manku, J. Sawada, A loopless Gray code for minimal signed-binary representations, 13th Annual European Symposium on Algorithms (ESA), LNCS 3669 (2005), 438-447.
T. D. Noe, Table of n, a(n) for n = 1..10000
C. Heuberger and H. Prodinger, On minimal expansions in redundant number systems: Algorithms and quantitative analysis, Computing 66(2001), 377-393.
S. Klavzar, U. Milutinovic and C. Petr, Stern polynomials, Adv. Appl. Math. 39 (2007) 86-95.
Maciej Ulas and Oliwia Ulas, On certain arithmetic properties of Stern polynomials, arXiv:1102.5109 [math.CO], 2011. See p. 10.
a(1)=0, a(2n)=a(n)+1, a(4n-1)=a(n)+1, a(4n+1)=a(n)+1 for n>=1 (Klavzar et al. Proposition 12). - Emeric Deutsch, Dec 04 2006
a(1)=0, a(2n)=a(n)+1, a(4n+1)=a(2n), a(4n+3)=a(2n+2) for n>=1 (Klavzar et al. Corollary 13). - Emeric Deutsch, Dec 04 2006
a(n) = A277329(n)-1. - Antti Karttunen, Oct 27 2016
a(34)=4, which counts these reductions: 34->17->4->2->1.
a:=proc(n) if n=1 then 0 elif n mod 2=0 then a(n/2)+1 elif n mod 4=1 then a((n-1)/2) else a((n+1)/2) fi end: seq(a(n), n=2..91); # Emeric Deutsch, Dec 04 2006
(PARI)
ep(r, n)=local(t=n/2^(r+2)); floor(t+5/6)-floor(t+4/6)-floor(t+2/6)+floor(t+1/6);
a(n)=sum(r=0, log(3*n)\log(2)-1, !ep(r, n)) ;
(Scheme, with memoization-macro definec)
(definec (A057526 n) (cond ((= 1 n) 0) ((even? n) (+ 1 (A057526 (/ n 2)))) ((= 3 (modulo n 4)) (+ 1 (A057526 (/ (+ 1 n) 4)))) (else (+ 1 (A057526 (/ (+ -1 n) 4))))))
;; Antti Karttunen, Oct 27 2016 after the first recurrence of Klavzar et al. as given by Emeric Deutsch in the Formula section.
Cf. A125184.
One less than A277329.
Clark Kimberling, Sep 03 2000
0 prepended by T. D. Noe, Feb 28 2011

redpajama_set_name: RedPajamaCommonCrawl


The open-ended War On Terror is yet another Big Lie. It is used as a cover for on-going imperialism, not only in Afghanistan and Iraq, but also in the Ukraine, Syria, and Palestine. Those who oppose Empire are said to be terrorists, and the monstrous crimes continue unabated. The most recent culprit is Israel -- thought to be an appendage (or driver?) of the U.S empire -- which is currently committing genocide in Palestine.
People are disinclined to believe monstrous crimes, especially since monstrous crimes are usually camouflaged by Big Lies. Hitler knew about the value of the Big Lie, and he wrote about it in Mein Kampf.
Stalin too was aware of the power of monstrous crimes. He is said to have observed that "when one man dies it is tragedy, when thousands die it's statistics."
People are even less inclined to believe that their own country perpetuates these crimes. On a subterranean level, people associate the search for truth as being somehow unpatriotic, even as it means a struggle for freedom from the totalitarian shackles of undemocratic governance that advances secret agendas through its manipulation of the masses.
Silence in the face of the lies of totalitarianism is a form of complicity.
Humanity's inclination to accept monstrous crimes and the lies that camouflage them means that the crimes are perpetuated, time and again, throughout history. Those responsible for engineering the crimes are aware of this.
We now know, for example, that the Bush and his aides lied 935 times in the lead up to the invasion of Iraq, and that they provided false evidence as a pretext for the invasion. Despite the fact that about one million people have already died as a result of this massive criminal enterprise, the public somehow remains delusional and ready to accept the next big crime and the next batch of lies.
The open-ended War On Terror is yet another Big Lie. It is used as a cover for on-going imperialism, not only in Afghanistan and Iraq, but also in the Ukraine, Syria, and Palestine. Those who oppose Empire are said to be terrorists, and the monstrous crimes continue unabated. The most recent culprit is Israel -- thought to be an appendage (or driver?) of the U.S empire -- which is currently committing genocide in Palestine. Predictably, much of the world is looking away, apparently lulled by the lies and distortions of the corporate media echo chambers.
In order to break this toxic cycle of delusions and crimes, we need to dig deep, find the money behind the lies, and the truth behind the crimes.
The foundational catalyst for the fraudulent "War On Terror" is 9/11, yet, unsurprisingly, investigations into this monstrous crime have so far been inadequate. As David Ray Griffith argues in The New Pearl Harbour: Disturbing Questions About The Bush Administration And 9/11, the conclusions of the political investigations: the 9/11 Joint Congressional Inquiry (of which 28 pages remain redacted), and the 9/11 Independent Commission, need to be re-examined by a sweeping juridical investigation, free from political interference and hasty presuppositions.
It is, for example, unacceptable, that the head of the 9/11 Commission, Philip Zelikow, was appointed by Bush administration, just as it is unacceptable that both the Joint Congressional hearings and the 9/11 Commission hearings were restricted by presuppositions that the crime was the result of breakdowns -- including incompetence, poor communication, and ignorance -- rather than official complicity.
•	Standard Operating Procedures (SOP) to intercept hijacked airlines were not followed on September 11, 2001 only.
•	Forensic evidence -- steel from the collapsed World Trade Center -- was quickly removed, and shipped overseas, before it could be examined.
•	Local Federal Bureau of Investigation (FBI) agents were prevented from professionally performing their duties by FBI headquarters.
A better appreciation of the "money behind the lies" adds urgency to the need for a judicial investigation into the terror of 9/11 that killed 2,996 people immediately, and launched (previously-planned) invasions overseas, all under the cover of the on-going and patently fraudulent War On Terror.
•	Bush's plans to enlarge the US Empire, and expand its globalized model of predatory economics were accelerated and advanced.
•	$48 billion was added to the Pentagon budget.
•	CIA funding was increased by 42 per cent.
•	Imperial conquests, starting with Afghanistan and Iraq, are now disguised as legitimate, and even "self-defence".
•	Legislation to dominate and oppress civilians (i.e the Patriot Act, Homeland Security) continues to flourish.
•	Security, policing, and military complexes continue to benefit from more funding and power.
•	Oil companies continue to profit and expand in new areas of US control.
Polities that benefit from a crime are not necessarily guilty of the crime; however, it is still reasonable to ask whether they contributed to the crime or whether they were simply unwitting "beneficiaries."
Justice and freedom demand that we ask these questions in the context of a wide-sweeping judicial investigation. Failing that, we should pay heed to George Orwell's assertion: "To be corrupted by totalitarianism, one does not have to live in a totalitarian country."


text:
Carl Heinrich Langbehn (* 6. Dezember 1901 in Padang-Bedagei, Sumatra, Niederländisch-Indien; † 12. Oktober 1944 in Berlin-Plötzensee) war ein deutscher Jurist, der sich als einer der führenden Rechtsanwälte Berlins in der Zeit des Nationalsozialismus für einzelne Regimegegner einsetzte. Seine Zugehörigkeit zum nationalkonservativen Kreis des deutschen Widerstands und seine persönliche Bekanntschaft mit Heinrich Himmler machten ihn zu einem Mittelsmann zwischen diesen Kreisen und möglicherweise auch zu Vertretern der Westmächte bei der Suche nach Verständigung im Zweiten Weltkrieg, seine Tätigkeit ist allerdings dokumentarisch kaum zu fassen und unterliegt Spekulationen. Langbehn wurde im September 1943 aus ungeklärten Gründen festgenommen und nach schwerer Folter und Verurteilung durch den Volksgerichtshof hingerichtet.

Der Rechtsanwalt
Langbehn wurde als Sohn eines deutschen Plantagenbesitzers in Niederländisch-Indien geboren und wurde ab dem neunten Lebensjahr bei Verwandten der Mutter in Deutschland erzogen. Langbehn studierte Rechtswissenschaft und wurde bei Fritz Pringsheim in Göttingen promoviert. Nach dem Referendariat in Celle und Berlin war er Pringsheims Assistent.

Um seine bis dahin prekäre finanzielle Situation zu verbessern, legte er die Prüfung zum Gerichtsassessor ab und wählte den Beruf des Rechtsanwaltes, den er bis zu seiner Festnahme am 22. oder 23. September 1943 ausübte. Gemeinsam mit seinem Studienkollegen Heinz Kleine eröffnete Langbehn 1932 eine eigene Kanzlei in der Neuen Wilhelmstraße, die ihm als "Springbrett" in höhere Gesellschaftskreise diente, darunter dem konservativen Deutschen Herrenklub. In den 1930er Jahren stieg Langbehn, der sich laut Pringsheim auf das Urheberrecht spezialisierte, durch seine Arbeit für die Filmbranche sowie als Patentanwalt zu "einem der bestverdienenden Rechtsanwälte seiner Zeit" auf und lebte in einer Villa in Berlin-Dahlem und einem Landsitz am Walchensee in großbürgerlichen Verhältnissen. Nach der Machtergreifung der Nazis verteidigte er Menschen, die unter den "neuen politischen Verhältnissen ausgegrenzt, angeklagt und verfolgt wurden."

Nach dem Reichstagsbrand bereitete Langbehn sich im Frühjahr 1933 darauf vor, den der Brandstiftung beschuldigten und inhaftierten Fraktionsvorsitzenden der Kommunistischen Partei Deutschlands Ernst Torgler zu verteidigen. Er lehnte das Mandat aber ab, als ihm von kommunistischer Seite unterstellt wurde, es ginge ihm nicht um die Gerechtigkeit, sondern um das Geld, das er mit dem prominenten Verteidigungsmandat verdienen könne. Kurze Zeit später übernahm Langbehn das Mandat zur Verteidigung für den in der Hitlerregierung und auch schon im Kabinett von Schleicher tätigen Reichskommissar für Arbeitsbeschaffung Günther Gereke. Diesen Vertreter der Republik von Weimar und Angehörigen der Christlich-Nationalen Bauern- und Landvolkpartei (CNBL) hatte das Hitler-Regime am 28. März 1933 unter dem erfundenen Vorwand der Veruntreuung verhaften lassen, um ihn aus der Reichsregierung zu entfernen. Langbehn war im Jahr 1932 wie Gereke Mitglied im Deutschen Herrenklub gewesen.

Langbehn war Mitglied der Deutschen Volkspartei gewesen und trat im Frühjahr 1933 der NSDAP bei.

Langbehns lernten 1936 oder 1938 Heinrich Himmler persönlich kennen. Als sie ihre Tochter Elke, eine Klassenkameradin von Himmlers Tochter Gudrun, vom Geburtstagsfest bei Himmlers abholten, lud Himmlers Frau das Paar Langbehn zu einem Tee ein, worauf es zu einem gemeinsamen Abendessen kam. Langbehn erzählte Himmler von Fällen aus seiner Anwaltstätigkeit, bei der er häufig erlebte, wie politisch verfolgte Menschen durch Gestapo und SS unrechtmäßig behandelt wurden. Er kritisierte Himmler dabei stark. Trotzdem fand Himmler Gefallen an dem jungen Anwalt und lud ihn häufiger zu sich ein. Die Tochter wurde einmal in den Ferien zu Gudruns Familie nach Bayern eingeladen. Langbehn konnte mit Himmler die politische Lage mit ungewöhnlicher Offenheit diskutieren.

Diese Verbindung zu Himmler konnte er erfolgreich einsetzen, als Fritz Pringsheim bei der Reichspogromnacht im  November 1938 ins Konzentrationslager gebracht worden war. Er bewirkte seine Freilassung und sorgte dann dafür,  dass Pringsheim seinen Pass zurückerhielt. Mit diesen Pass konnte Pringsheim dann im April 1939 nach England in die Freiheit auswandern. Weiteren verhafteten Juden konnte Langbehn unter "großen Vermögensopfern" zur Auswanderung verhelfen. Auch für andere Verfolgte setzte sich Langbehn ein: 1940 verdankte der Bauer Kaspar Danner aus Jachenau, der dem Harnier-Kreis angehörte, seine Entlassung aus politischer Haft der unmittelbaren Einflussnahme Langbehns. Langbehn erreichte 1940 auch die Entlassung seines Mandanten, des  Widerstandskämpfers Albrecht Graf von Bernstorff,  aus dem KZ Dachau. Im Jahr 1942 vertrat er den wegen seiner regimekritischen Satire verhafteten Kabarettisten Werner Finck.

Zwischen Widerstand und Himmler
Himmler war zwar eine der Stützen des NS-Regimes und im Lauf des Krieges mit stets ausgeweiteten Befugnissen und Aufgaben betraut, darunter insbesondere der Organisation des Holocaust, einzelne Historiker nehmen jedoch an, dass er ab 1943 starke Zweifel am "Endsieg" hegte und insgeheim Friedenskontakte zu den westlichen Alliierten suchte. Diese Deutung wird allerdings von den meisten Historikern, darunter dem Himmler-Experten Peter Longerich, abgelehnt. Himmler setzte Langbehn in verschiedenen Sonderaufträgen zur auswärtigen Materialbeschaffung ein und machte sich möglicherweise auch Langbehns Kontaktaufnahmen zu alliierten Mittelsmännern zunutze, auch wenn Intentionen und Strategien Himmlers wegen des Fehlens zuverlässiger Dokumente Spekulation bleiben. So traf Langbehn mit Wissen Himmlers Professor Bruce C. Hopper Ende 1942 in Stockholm, der für den US-Geheimdienst Office of Strategic Services (OSS) arbeitete. Auch sprach er mit einem "offiziellen Engländer in Zürich". Beide Gespräche schienen auf Verhandlungsmöglichkeiten hinzudeuten.

Langbehn stand aber auch mit dem Widerstand in Verbindung, vor allem mit Johannes Popitz und Ulrich von Hassell. Beide brachte er im August 1941 mit dem Schweizer Diplomaten Carl Burckhardt zusammen. Zu dritt erörterte man Friedensmöglichkeiten, zumal Burckhardt demnächst Winston Churchill treffen sollte.

Die vage Kenntnis Himmlers von der Widerstandsbewegung und das Motiv seines Nichteinschreitens sind mangels zuverlässiger Unterlagen ebenfalls Gegenstand von Spekulationen. Möglicherweise gedachte er den Widerstand zu benutzen, um Hitler zu beseitigen und dann selbst die Macht in Deutschland zu übernehmen. Hassell und Popitz verfolgten ausweislich des Tagebuchs Hassells, wenn auch als "Verzweiflungsschritt", den umgekehrten Gedanken: Himmler und die SS sollten einen Umsturz geschehen lassen, um den Krieg zu beenden. Dann würde man sich dieser wieder entledigen.

Für den 26. August 1943 organisierte Langbehn ein Gespräch zwischen Popitz und Himmler. Popitz deutete darin Himmler gegenüber an, Hitler solle sich aus der aktiven Politik zurückziehen, und es müsse Friedensverhandlungen mit den Westmächten geben. Himmler schien dieser gegen Hitler gerichteten Aktion wohlwollend gegenüberzustehen. Im September 1943 reiste Langbehn im Auftrag Himmlers nach Bern, um mit dem OSS unter Leitung von Allen Welsh Dulles Kontakt aufzunehmen. Nach seiner Rückkehr berichtete er Himmler.

Haft, Verurteilung und Tod
Kurz nach dem Gespräch wurde Langbehn am 22. oder 23. September 1943 im Gestapohauptquartier aus unbekannten Gründen verhaftet, als er wegen seines Mandanten Albrecht Graf von Bernstorff vorsprach, der kurz zuvor wieder verhaftet worden war. Verschiedene Quellen berichten, dass die Gestapo angeblich einen Bericht der Alliierten über Langbehns Treffen in Bern habe dechiffrieren können, woraufhin Himmler Langbehn habe fallenlassen; Nach dem Attentat vom 20. Juli 1944 behauptete Himmler in Posen, er habe Langbehn wegen seiner Widerstandskontakte festnehmen lassen. Auch Langbehns Ehefrau Irmgard und seine Mitarbeiterin Marie-Louise Sarre waren verhaftet worden, wie der in der Nachbarschaft lebende Peter Bielenberg seiner Frau Christabel berichtete.

Carl Langbehn wurde vermutlich Anfang 1944 erst ins KZ Sachsenhausen und dann ins KZ Ravensbrück verlegt. Nach der Festnahme von Popitz am 21. Juni 1944 wurde gegen beide von Oberreichsanwalt Ernst Lautz am 25. September 1944 Anklage auf Hoch- und Landesverrat erhoben. Die Beschuldigten hätten Hitler beseitigen, die Macht an sich reißen und in einem würdelosen Pakt mit dem Feind Frieden schließen wollen. Am 3. Oktober fand die Verhandlung vor dem Volksgerichtshof unter Roland Freisler statt. Sie musste, wie Ernst Kaltenbrunner dem Reichsjustizminister Otto Georg Thierack mitteilte, "im Hinblick auf den auch Ihnen bekannten Sachverhalt, nämlich Besprechung RFSS-Popitz … unter praktischem Ausschluss der Öffentlichkeit durchgeführt werden". Popitz und Langbehn wurden zum Tod verurteilt.

Joseph Goebbels schrieb zwar in seinem Tagebuch am 6. Oktober, die Hinrichtung solle "vorläufig noch ausgespart" werden, denn Langbehn habe "noch eine ganze Menge von sensationellen Neuigkeiten mitzuteilen", doch die Hinrichtung wurde schon am 12. Oktober 1944 in Plötzensee durch den Strang  vollzogen. Vorher war Langbehn mehrere Tage qualvoll gefoltert worden, worüber der Widerstandskämpfer Rainer Hildebrandt 1949 berichtete:
"'Obergruppenführer' Müller, mit dessen Höflichkeit es wenige Gestapobeamten aufnehmen konnten, zog es vor, nicht selbst zu foltern. Stattdessen ließ er dem Gefangenen die Hände auf dem Rücken zusammenbinden und ihm mittels einer an der Decke angebrachten Zugvorrichtung die Arme hochziehen. Als Rechtsanwalt Dr. Langbehn, der diese Foltermethode schweigend überstand, mit zerrissenen Schultergelenkmuskeln zur Hinrichtung geführt wurde, konnte ein Wachmann mit Recht sagen: 'An Langbehn ist nicht mehr viel zu töten, das hat Müller schon getan.'"

Schriften
 Zustimmung und Verfügung. Rechts- und staatswissenschaftliche Dissertation, Universität Göttingen, 1925, .
 Schutzschrift in der Strafsache gegen den Landrat a. D. Dr. Günther Gereke. Berlin 1933, .

Quellen
 Friedrich Freiherr Hiller von Gaertringen (Hrsg.): Die Hassell-Tagebücher 1938–1944. Aufzeichnungen vom Anderen Deutschland. Berlin 1988, ISBN 3-88680-017-2.
 Christabel Bielenberg: Als ich Deutsche war: 1934–1945. Eine Engländerin erzählt. Autorisierte deutsche Fassung von Christian Spiel. Beck, München 1987, ISBN 3-406-31919-X.

Literatur
 Allen Welsh Dulles: Verschwörung in Deutschland. Übersetzung und Nachwort Wolfgang von Eckhardt. Europa, Zürich 1948 (Harriet Schieber, Kassel 1949; Originalausgabe: Germany's Underground. New York 1947). (Das Buch enthält eine Kopie der Anklageakte gegen Langbehn).

 Winfried Meyer: Carl Langbehn. In: ders. (Hrsg.): Verschwörer im KZ (= Schriftenreihe der Stiftung Brandenburgische Gedenkstätten. Bd. 5). Edition Hentrich, Berlin 1999, S. 300–307.
 Claus Langbehn: Das Spiel des Verteidigers – Der Jurist Carl Langbehn im Widerstand gegen den Nationalsozialismus. (= Schriften der Gedenkstätte Deutscher Widerstand. Reihe A: Analysen und Darstellungen. Bd. 8). Lukas, Berlin 2014, ISBN 978-3-86732-203-4 (Vorschau).

Weblinks

  (mit Bild)
 Bild Langbehns vor dem Volksgerichtshof beim United States Holocaust Memorial Museum

Einzelnachweise

Rechtsanwalt (Deutschland)
Hingerichtete Person (NS-Opfer)
DVP-Mitglied
NSDAP-Mitglied
Deutscher
Geboren 1901
Gestorben 1944
Mann

redpajama_set_name: RedPajamaWikipedia


The annual Grawemeyer Awards program at the University of Louisville pays tribute to the power of creative ideas, emphasizing the impact a single idea can have on the world. This year, Andrew Norman's orchestral piece "Play" is the winner of the Grawemeyer Award for Music Composition.
The music of Norman, a Los Angeles-based composer, has been performed by leading ensembles worldwide, such as the Los Angeles Philharmonic, New York Philharmonic, Royal Concertgebouw and the Orchestre National de France. He recently was named Musical America's 2017 Composer of the Year.
In three movements, "Play" explores the relationship of choice and chance, free will and control. It investigates the ways musicians in an orchestra can play with, against, or apart from one another; and maps concepts from the world of video gaming onto traditional symphonic structures.
I spoke with Norman about the format of the composition, how decreased audience attention spans informs his work, and the importance of the work of modern composers. Listen to our conversation in the player above.


text:
package http

import (
	"go-common/app/job/main/account-recovery/conf"
	"go-common/app/job/main/account-recovery/service"
	"go-common/library/log"
	bm "go-common/library/net/http/blademaster"
)

var (
	srv *service.Service
)

// Init init
func Init(c *conf.Config) {
	srv = service.New(c)
	engine := bm.DefaultServer(c.BM)
	router(engine)
	if err := engine.Start(); err != nil {
		log.Error("engine.Start error(%v)", err)
		panic(err)
	}
}

func router(e *bm.Engine) {
	e.Ping(ping)
	e.Register(register)
	g := e.Group("/x/account-recovery")
	{
		g.GET("/test", ping)
	}
}

func ping(c *bm.Context) {
}

func register(c *bm.Context) {
	c.JSON(map[string]interface{}{}, nil)
}

redpajama_set_name: RedPajamaGithub


text:
Captain Malcolm Cosmo Bonsor1
M, #172031, b. 2 August 1878, d. 10 March 1918
Captain Malcolm Cosmo Bonsor was born on 2 August 1878.1 He was the son of Sir Henry Cosmo Orme Bonsor, 1st Bt. and Emily Gertrude Fellowes.1 He married Sybil Henrietta Fergusson, daughter of Charles Robert Kenneth Fergusson and Eleanora Dalrymple Davidson, on 6 December 1906.1 He died on 10 March 1918 at age 39, killed in action.1
He gained the rank of Captain in the Norfolk Yeomanry.1 He fought in the First World War.1
Sybil Henrietta Fergusson1
F, #172032, d. 7 February 1966
Sybil Henrietta Fergusson was the daughter of Charles Robert Kenneth Fergusson and Eleanora Dalrymple Davidson.1,2 She married, firstly, Captain Malcolm Cosmo Bonsor, son of Sir Henry Cosmo Orme Bonsor, 1st Bt. and Emily Gertrude Fellowes, on 6 December 1906.1 She married, secondly, Mervyn Adrian Touchet Ridley, son of Arthur William Ridley and Adriana Elizabeth Newton, on 12 May 1920.1 She died on 7 February 1966.2
From 6 December 1906, her married name became Bonsor.1 From 12 May 1920, her married name became Ridley.1
Child of Sybil Henrietta Fergusson and Mervyn Adrian Touchet Ridley
Susan Frances Mary Ridley+2 b. 22 Feb 1921
Charles Robert Kenneth Fergusson1
M, #172033, b. 25 September 1842, d. 24 October 1926
Charles Robert Kenneth Fergusson was born on 25 September 1842.1 He was the son of Hew Dalrymple Hamilton Fergusson and Louisa Godby.2 He married, firstly, Eleanora Dalrymple Davidson, daughter of Duncan Davidson, 4th of Tulloch and Eleanora Dalrymple Fergusson, on 5 December 1872.1 He married Alice Eliza Grant, daughter of Admiral Henry Duncan Grant, on 6 April 1907.1 He died on 24 October 1926 at age 84.1
He gained the rank of Officer in the 79th Highlanders.1 He gained the rank of Captain in the Cameron Highlanders.2 He lived at The Hll, Creetown, Kirkcudbrightshire, ScotlandG.2
Children of Charles Robert Kenneth Fergusson and Eleanora Dalrymple Davidson
Sybil Henrietta Fergusson+2 d. 7 Feb 1966
Muriel Catherine Fergusson2 d. 6 Aug 1909
Mervyn Adrian Touchet Ridley1
M, #172034, b. 1886, d. 21 December 1951
Mervyn Adrian Touchet Ridley was born in 1886.2 He was the son of Arthur William Ridley and Adriana Elizabeth Newton.3 He married Sybil Henrietta Fergusson, daughter of Charles Robert Kenneth Fergusson and Eleanora Dalrymple Davidson, on 12 May 1920.1 He died on 21 December 1951.2
He was educated at Eton College, Windsor, Berkshire, England.2 He gained the rank of Lieutenant in the Grenadier Guards.2 He fought in the First World War between 1914 and 1919.2 He was awarded the Military Cross (M.C.)2
Child of Mervyn Adrian Touchet Ridley and Sybil Henrietta Fergusson
[S2226] Lawless Bethune, "re: Bethune Family," e-mail message to www.thepeerage.com, 27 March 2007. Hereinafter cited as "re: Bethune Family."
Robert Cecil Bonsor1
M, #172035, b. 20 November 1880, d. 3 March 1932
Robert Cecil Bonsor was born on 20 November 1880.1 He was the son of Sir Henry Cosmo Orme Bonsor, 1st Bt. and Emily Gertrude Fellowes.1 He married Eugenia Constance Frances Pattinson Lawrence, daughter of S. Lawrence, on 25 November 1922.1 He died on 3 March 1932 at age 51.1
He fought in the First World War.1 He gained the rank of Lieutenant in the Welsh Guards.1 He was awarded the Military Cross (M.C.)1
Children of Robert Cecil Bonsor and Eugenia Constance Frances Pattinson Lawrence
Ann Elizabeth Bonsor2 b. 22 Sep 1923, d. 25 Apr 2014
Michael Cosmo Bonsor+2 b. 10 May 1926, d. 20 Aug 2005
Eugenia Constance Frances Pattinson Lawrence1
Eugenia Constance Frances Pattinson Lawrence was the daughter of S. Lawrence.1 She married Robert Cecil Bonsor, son of Sir Henry Cosmo Orme Bonsor, 1st Bt. and Emily Gertrude Fellowes, on 25 November 1922.1 She died on 13 July 1958.1
From 25 November 1922, her married name became Bonsor.1
Children of Eugenia Constance Frances Pattinson Lawrence and Robert Cecil Bonsor
S. Lawrence1
Child of S. Lawrence
Eugenia Constance Frances Pattinson Lawrence+1 d. 13 Jul 1958
Lila Joyce Nayler1
F, #172038, b. 26 February 1922, d. 3 March 1985
Lila Joyce Nayler was born on 26 February 1922.1 She was the daughter of Catherine Corry Suddaby.1 She married Cyril Mills Abbott, son of Arthur Mills Abbott and Sarah Daisy Rye, on 6 April 1940.1 She died on 3 March 1985 at age 63.2
She was given the name of Beverley Suddaby at birth.1 She was adopted by Robert Nayler and Louise (?) on 12 November 1923.1 From 6 April 1940, her married name became Abbott.1
Children of Lila Joyce Nayler and Cyril Mills Abbott
Robert Mills Abbott+1 b. 4 Jul 1941
Gary Arthur Abbott1 b. 9 Aug 1944
Lavina Joyce Abbott1 b. 17 Nov 1949
Julie Ann Abbott1 b. 30 Dec 1952
[S1506] Robert Abbott, "re: Catherine Suddaby," e-mail message to Darryl Roger Lundy, 22 October 2005. Hereinafter cited as "re: Catherine Suddaby."
[S3174] Robert Lloyd-Jones, "re: Abbott Family," e-mail message to Darryl Roger Lundy, 3 June 2008. Hereinafter cited as "re: Abbott Family."
Cyril Mills Abbott1
M, #172039, b. 8 August 1918, d. 23 July 1982
Cyril Mills Abbott was born on 8 August 1918.1 He was the son of Arthur Mills Abbott and Sarah Daisy Rye.1 He married Lila Joyce Nayler, daughter of Catherine Corry Suddaby, on 6 April 1940.1 He died on 23 July 1982 at age 63.1
Children of Cyril Mills Abbott and Lila Joyce Nayler
Robert Mills Abbott1
M, #172040, b. 4 July 1941
Robert Mills Abbott was born on 4 July 1941.1 He is the son of Cyril Mills Abbott and Lila Joyce Nayler.1 He married Esme Jeanette Heiniger on 20 April 1963.1
Children of Robert Mills Abbott and Esme Jeanette Heiniger
Glen Abbott2
Angela Abbott2
Craig Mills Abbott+2 b. 8 Sep 1963


On Thursday, 6 th December 2018, the Holy See and the Government of the UAE announced the visit of Pope Francis to Abu Dhabi from the late evening of 3 rd February to midday of 5 th February 2019. We are happy and grateful that Pope Francis accepted the invitation of His Highness Sheikh Mohammed bin Zayed Al Nahyan, Crown Prince of Abu Dhabi, to participate in the "International Interfaith Meeting on Human Fraternity". At the same time, the visit is also an answer to the invitation addressed to Pope Francis by the Catholic Church in the UAE.


The Financial Intelligence Unit conducted more than 3,000 inquiries into the financing of terrorism last year, Russian Foreign Ministry Department on New Threats and Challenges Deputy Director Dmitry Feoktistov said at a special meeting of the UN Security Council Counter-Terrorism Committee in New York. His speech was published on the Russian Foreign Ministry's website.
"The Financial Intelligence Unit held more than 3,000 investigations into the financing of terrorism in 2011, and 2,500 materials were sent to law enforcement agencies for verification. In fulfillment of Russia's international commitments to blocking the financing of terrorism, the list of organizations and individuals engaged in terrorism and terrorism is constantly updated," he said.
The list's international segment contains names of 106 organizations and 394 individuals (on the basis of UN Security Council resolutions), while the Russian segment of the document lists 48 organizations and 2,382 individuals, he said. A key element of the national system counteracting terrorism and money laundering is the Federal Financial Monitoring Service - a Russian unit of financial intelligence.
"Russia stands for constant strengthening of relevant mechanisms, among them the FATF (an intergovernmental organization combating money laundering) and FATF-based regional groups that unite over 180 states," the department deputy director said.
The FATF mandate for 2012-2022, which was drafted with the active participation of Russia, implies that the Financial Action Task Force will not only handle its traditional issues but also react to the new threats to the integrity of the global financial system, which may be identified by the international community, primarily the UN Security Council.
The diplomat said first experience had been gained: a FATF recommendation on counteracting financing of the proliferation of weapons of mass destruction had been approved. "Its role will be strictly auxiliary: it will assist and monitor the national implementation of financial provisions of UN Security Council resolutions with regard to the proliferation of weapons of mass destruction defined as a threat to world peace and security. The FATF is also focused on the search for answers to other transboundary threats, including corruption and sea piracy," Feoktistov said.


text:
Fans petition Scarlet Johansson to be removed from Ghost In The Shell remake
Animation Comics TV
"What If...?" first look promo opens up the Marvel-verse to a world of possibilities
Animation Gaming TV
"Resident Evil: Infinite Darkness" series is said to be 'canon'
Animation TV
Netflix announces a new Godzilla anime series
Wacko, Jacko and Dot are back. "The Animaniacs" returns in new clip
It was only a matter of time. With a GITS feature finally in the works, it was somewhat a disappointment in my humble opinion that a film that lends itself to the iconic Japanese characters and cyberpunk Japanese cityscapes would be Anglicised in a remake was beyond a lot of us.
Scarlett Johannson was cast to play the lead for the film adaptation of Ghost In The Shell, and fans of the cult classic 1995 Masamune Shirow's cyberpunk anime have started a petition to have Scarlet Johansson removed from the project. Fans obviously feel that her casting as Major Motoko Kusanagi (who is Asian) is whitewashing the classic anime, and cutting out Asian actors from an industry that is already stacked against them:
Fans of the iconic 1995 animated Japanese sci-fi film Ghost in the Shell have been anticipating a live-action remake for years – but now, instead of casting an Asian actress, Dreamworks has selected Scarlett Johansson for the lead role! The film revolves around Major Motoko Kusanagi, a member of a futuristic security force tasked with tracking a mysterious hacker.
The original film is set in Japan, and the major cast members are Japanese. So why would the American remake star a white actress? The industry is already unfriendly to Asian actors without roles in major films being changed to exclude them. One recent survey found that in 2013, Asian characters made up only 4.4% of speaking roles in top-grossing Hollywood films.
Dreamworks could be using this film to help provide opportunities for Asian-American actors in a market with few opportunities for them to shine – please sign the petition asking them to reconsider casting Scarlett Johansson in Ghost in the Shell and select actors who are truer to the cast of the original film!
With Hollywood, a lot of times it comes down to numbers, and if Scarlet Johansson is their ticket to swollen cinema openings, then they may ignore everything else. Unless the fans come out in their number to boycott the film, where Dreamworks may compromise. Time will tell. The same goes for the Akira remake if that film happens.
Dreamworks Ghost in the Shell live-action film is scheduled for release on April 14th 2017
So what do you think? Is it a big deal that an Asian is being replaced in a film adaptation just to bring more of an audience in? Or should the integrity of the original anime should be upheld at all costs? Let us know what you think in the comments below
Related Topics:cyber punkdreamworksghost in the shellgitsjapanMasamune ShirowpetitionScarlett Johannson
Drax, Gamora and Star-Lord + confirmed for Guardians Of The Galaxy series
Kevin Michael Richardson to voice Groot in Guardians Of the Galaxy animated series
Netflix teases "Ghost In The Shell SAC_2045" with anime image at Annecy Film Fest
Netflix heralds the return of the Major as a new "Ghost In The Shell" project is in the works
DREAMWORKS VOLTRON LEGENDARY DEFENDER releases a Season 8 teaser trailer
Netflix's "She-Ra" animated series reveals its first look images
"The Fast and The Furious" franchise is drifting on to Netflix as an animated series
"Voltron: Legendary Defender" season 5 trailer lands online


text:
Q: "shake is not supported on real devices" error message I'm using a keyword "Shake Device" for iOS v. 11.4.1 and using XCUITest driver and appium version 1.13. I get this error message

shake is not supported on real devices.

According to appium.io it should be supported
http://appium.io/docs/en/commands/device/interactions/shake/
The Code I used is driver.ShakeDevice(); using C#.
Did anyone try this before?

A: As per http://appium.io/docs/en/commands/device/interactions/shake/
followig syntax for shake:
driver.shake();

redpajama_set_name: RedPajamaStackExchange


text:
Long-term implementation of the CATCH physical education program
Steven H. Kelder, Paul D. Mitchell, Thomas L. McKenzie, Carol Derby, Patricia K. Strikmiller, Russell V. Luepker, Elaine J. Stone
To test the effectiveness of the Child and Adolescent Trial for Cardiovascular Health (CATCH) program, a randomized trial was conducted in 96 elementary schools in four regions of the United States. Results from the original trial indicated a significant positive effect on the delivery of physical education (PE). All 56 former intervention schools (FI), 20 randomly selected former control schools (FC), and 12 newly selected unexposed control schools (UC) were assessed 5 years postintervention. Results indicate a strong secular trend of increasing moderate to vigorous physical activity (MVPA) in PE classes among both FC and UC schools. The FI schools surpassed the Healthy People 2010 goal for MVPA during PE lesson time (i.e., 50%), whereas the FC and UC schools came close to it. Barriers to implementing CATCH PE included insufficient training and lower importance of PE compared to other academic areas and indicate the need for in-service training.
Health Education and Behavior
Institutionalization
School-based interventions
Dive into the research topics of 'Long-term implementation of the CATCH physical education program'. Together they form a unique fingerprint.
Physical Education Arts & Humanities 100%
Education Program Arts & Humanities 84%
Health Education Medicine & Life Sciences 74%
physical education Social Sciences 66%
Health Arts & Humanities 48%
adolescent Social Sciences 43%
health Social Sciences 34%
Kelder, S. H., Mitchell, P. D., McKenzie, T. L., Derby, C., Strikmiller, P. K., Luepker, R. V., & Stone, E. J. (2003). Long-term implementation of the CATCH physical education program. Health Education and Behavior, 30(4), 463-475. https://doi.org/10.1177/1090198103253538
Long-term implementation of the CATCH physical education program. / Kelder, Steven H.; Mitchell, Paul D.; McKenzie, Thomas L. et al.
In: Health Education and Behavior, Vol. 30, No. 4, 08.2003, p. 463-475.
Kelder, SH, Mitchell, PD, McKenzie, TL, Derby, C, Strikmiller, PK, Luepker, RV & Stone, EJ 2003, 'Long-term implementation of the CATCH physical education program', Health Education and Behavior, vol. 30, no. 4, pp. 463-475. https://doi.org/10.1177/1090198103253538
Kelder SH, Mitchell PD, McKenzie TL, Derby C, Strikmiller PK, Luepker RV et al. Long-term implementation of the CATCH physical education program. Health Education and Behavior. 2003 Aug;30(4):463-475. doi: 10.1177/1090198103253538
Kelder, Steven H. ; Mitchell, Paul D. ; McKenzie, Thomas L. et al. / Long-term implementation of the CATCH physical education program. In: Health Education and Behavior. 2003 ; Vol. 30, No. 4. pp. 463-475.
@article{97ccedc1b8db4a26afa6dfda0cac58dd,
title = "Long-term implementation of the CATCH physical education program",
abstract = "To test the effectiveness of the Child and Adolescent Trial for Cardiovascular Health (CATCH) program, a randomized trial was conducted in 96 elementary schools in four regions of the United States. Results from the original trial indicated a significant positive effect on the delivery of physical education (PE). All 56 former intervention schools (FI), 20 randomly selected former control schools (FC), and 12 newly selected unexposed control schools (UC) were assessed 5 years postintervention. Results indicate a strong secular trend of increasing moderate to vigorous physical activity (MVPA) in PE classes among both FC and UC schools. The FI schools surpassed the Healthy People 2010 goal for MVPA during PE lesson time (i.e., 50%), whereas the FC and UC schools came close to it. Barriers to implementing CATCH PE included insufficient training and lower importance of PE compared to other academic areas and indicate the need for in-service training.",
keywords = "Children, Health promotion, Institutionalization, Physical education, School-based interventions",
author = "Kelder, {Steven H.} and Mitchell, {Paul D.} and McKenzie, {Thomas L.} and Carol Derby and Strikmiller, {Patricia K.} and Luepker, {Russell V.} and Stone, {Elaine J.}",
journal = "Health Education Quarterly",
T1 - Long-term implementation of the CATCH physical education program
AU - Kelder, Steven H.
AU - Mitchell, Paul D.
AU - McKenzie, Thomas L.
AU - Derby, Carol
AU - Strikmiller, Patricia K.
AU - Luepker, Russell V.
AU - Stone, Elaine J.
N2 - To test the effectiveness of the Child and Adolescent Trial for Cardiovascular Health (CATCH) program, a randomized trial was conducted in 96 elementary schools in four regions of the United States. Results from the original trial indicated a significant positive effect on the delivery of physical education (PE). All 56 former intervention schools (FI), 20 randomly selected former control schools (FC), and 12 newly selected unexposed control schools (UC) were assessed 5 years postintervention. Results indicate a strong secular trend of increasing moderate to vigorous physical activity (MVPA) in PE classes among both FC and UC schools. The FI schools surpassed the Healthy People 2010 goal for MVPA during PE lesson time (i.e., 50%), whereas the FC and UC schools came close to it. Barriers to implementing CATCH PE included insufficient training and lower importance of PE compared to other academic areas and indicate the need for in-service training.
AB - To test the effectiveness of the Child and Adolescent Trial for Cardiovascular Health (CATCH) program, a randomized trial was conducted in 96 elementary schools in four regions of the United States. Results from the original trial indicated a significant positive effect on the delivery of physical education (PE). All 56 former intervention schools (FI), 20 randomly selected former control schools (FC), and 12 newly selected unexposed control schools (UC) were assessed 5 years postintervention. Results indicate a strong secular trend of increasing moderate to vigorous physical activity (MVPA) in PE classes among both FC and UC schools. The FI schools surpassed the Healthy People 2010 goal for MVPA during PE lesson time (i.e., 50%), whereas the FC and UC schools came close to it. Barriers to implementing CATCH PE included insufficient training and lower importance of PE compared to other academic areas and indicate the need for in-service training.
KW - Institutionalization
KW - Physical education
KW - School-based interventions
JO - Health Education Quarterly
JF - Health Education Quarterly