A tool for knowledge-oriented physics-based motion planning and simulation

The book covers a variety of topics in Information and Communications Technology (ICT) and their impact on innovation and business. The authors discuss various innovations, business and industrial motivations, and impact on humans and the interplay between those factors in terms of finance, demand, and competition. Topics discussed include the convergence of Machine to Machine (M2M), Internet of Things (IoT), Social, and Big Data. They also discuss AI and its integration into technologies from machine learning, predictive analytics, security software, to intelligent agents, and many more. Contributions come from academics and professionals around the world. Covers the most recent practices in ICT related topics pertaining to technological growth, innovation, and business; Presents a survey on the most recent technological areas revolutionizing how humans communicate and interact; Features four sections: IoT, Wireless Ad Hoc & Sensor Networks, Fog Computing, and Big Data Analytics.(Chapter) The recent advancements in robotic systems set new challenges for robotic simulation software, particularly for planning. It requires the realistic behavior of the robots and the objects in the simulation environment by incorporating their dynamics. Furthermore, it requires the capability of reasoning about the action effects. To cope with these challenges, this study proposes an open-source simulation tool for knowledge-oriented physics-based motion planning by extending The Kautham Project, a C++ based open-source simulation tool for motion planning. The proposed simulation tool provides a flexible way to incorporate the physics, knowledge and reasoning in planning process. Moreover, it provides ROS-based interface to handle the manipulation actions (such as push/pull) and an easy way to communicate with the real robotsPeer ReviewedPostprint (author's final draft

Perceptual and Semantic Processing in Cognitive Robots

The challenge in human–robot interaction is to build an agent that can act upon human implicit statements, where the agent is instructed to execute tasks without explicit utterance. Understanding what to do under such scenarios requires the agent to have the capability to process object grounding and affordance learning from acquired knowledge. Affordance has been the driving force for agents to construct relationships between objects, their effects, and actions, whereas grounding is effective in the understanding of spatial maps of objects present in the environment. The main contribution of this paper is to propose a methodology for the extension of object affordance and grounding, the Bloom-based cognitive cycle, and the formulation of perceptual semantics for the context-based human–robot interaction. In this study, we implemented YOLOv3 to formulate visual perception and LSTM to identify the level of the cognitive cycle, as cognitive processes synchronized in the cognitive cycle. In addition, we used semantic networks and conceptual graphs as a method to represent knowledge in various dimensions related to the cognitive cycle. The visual perception showed average precision of 0.78, an average recall of 0.87, and an average F1 score of 0.80, indicating an improvement in the generation of semantic networks and conceptual graphs. The similarity index used for the lingual and visual association showed promising results and improves the overall experience of human–robot interaction

Artificial Subjectivity: Personal Semantic Memory Model for Cognitive Agents

Personal semantic memory is a way of inducing subjectivity in intelligent agents. Personal semantic memory has knowledge related to personal beliefs, self-knowledge, preferences, and perspectives in humans. Modeling this cognitive feature in the intelligent agent can help them in perception, learning, reasoning, and judgments. This paper presents a methodology for the development of personal semantic memory in response to external information. The main contribution of the work is to propose and implement the computational version of personal semantic memory. The proposed model has modules for perception, learning, sentiment analysis, knowledge representation, and personal semantic construction. These modules work in synergy for personal semantic knowledge formulation, learning, and storage. Personal semantics are added to the existing body of knowledge qualitatively and quantitatively. We performed multiple experiments where the agent had conversations with the humans. Results show an increase in personal semantic knowledge in the agent’s memory during conversations with an F1 score of 0.86. These personal semantics evolved qualitatively and quantitatively with time during experiments. Results demonstrated that agents with the given personal semantics architecture possessed personal semantics that can help the agent to produce some sort of subjectivity in the future

A tool for knowledge-oriented physics-based motion planning and simulation

The book covers a variety of topics in Information and Communications Technology (ICT) and their impact on innovation and business. The authors discuss various innovations, business and industrial motivations, and impact on humans and the interplay between those factors in terms of finance, demand, and competition. Topics discussed include the convergence of Machine to Machine (M2M), Internet of Things (IoT), Social, and Big Data. They also discuss AI and its integration into technologies from machine learning, predictive analytics, security software, to intelligent agents, and many more. Contributions come from academics and professionals around the world. Covers the most recent practices in ICT related topics pertaining to technological growth, innovation, and business; Presents a survey on the most recent technological areas revolutionizing how humans communicate and interact; Features four sections: IoT, Wireless Ad Hoc & Sensor Networks, Fog Computing, and Big Data Analytics.(Chapter) The recent advancements in robotic systems set new challenges for robotic simulation software, particularly for planning. It requires the realistic behavior of the robots and the objects in the simulation environment by incorporating their dynamics. Furthermore, it requires the capability of reasoning about the action effects. To cope with these challenges, this study proposes an open-source simulation tool for knowledge-oriented physics-based motion planning by extending The Kautham Project, a C++ based open-source simulation tool for motion planning. The proposed simulation tool provides a flexible way to incorporate the physics, knowledge and reasoning in planning process. Moreover, it provides ROS-based interface to handle the manipulation actions (such as push/pull) and an easy way to communicate with the real robotsPeer Reviewe

A lightweight perception module for planning purposes

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksSensing is an essential component for robots to perform the manipulation tasks in real environments. This study proposes a lightweight deep-learning-based sensing modules which allows the robots to automatically model the workspace for manipulation planning. This sensing module is developed as a part of our ongoing manipulation planning framework. It will be used to enhance the sensing accuracy and make it capable of planning the manipulation tasks in real environments. The retrained model is further trained over commonly used objects to enhance the prediction accuracy.Peer ReviewedPostprint (author's final draft

Learning Action-oriented grasping for manipulation

Complex manipulation tasks require grasping strategies that simultaneously satisfy the stability and the semantic constraints that have to be satisfied for an action to be feasible, referred as action-oriented semantic grasp strategies. This study develops a framework using machine learning techniques to compute action-oriented semantic grasps. It takes a 3D model of the object and the action to be performed as input and provides a vector of action-oriented semantic grasps. We evaluate the performance of machine learning (particu- larly classification techniques) to determine which approaches perform better for this problem. Using the best approaches, a multi-model classification technique is developed. The proposed approach is evaluated in simulation to grasp different kitchenobjects using a parallel gripper. The results show that multi-model classification approach enhances the prediction accuracy. The implemented system can be used as to automate the data labeling process required for deep learning approaches.Peer ReviewedPostprint (author's final draft

A Step Towards the Development of Socio-cognitive Agent

The embodiment of consciousness in socio-cognitive agents play a significant role in their acceptance as co-partners. Man-machine social interaction’s success is based on the agent’s generated believable behaviors. In this regard, such an agent needs to generate realistic beliefs, intentions, goals, self-regulation, verbal, and non-verbal communication (including gestures) according to the context of ongoing interaction is very important. This study hypothesizes that the implementation of the Theory of mind (TOM) may allow the agent to change its intentions, beliefs, and desires by predicting the existing perspective and mental states of the other agents involved in the given social interaction. To study the complexity of dynamics in a social context we have taken the case study of the ‘paper-scissor-rock’ game and developed a cognitive agent capable of using gestures for non-verbal communication following the Theory of mind. This work is in progress and is being developed on the iCub robot simulation. We have used tapped delay line neural networks as the basis for reinforcement learning and strategic planning. This paper will report the cognitive model, neural network initial results obtained