The paper presents a novel cloud-based digital twin learning platform for teaching and training concepts of cognitive robotics. Instead of forcing interested learners or students to install a new operating system and bulky, fragile software onto their personal laptops just to solve tutorials or coding assignments of a single lecture on robotics, it would be beneficial to avoid technical setups and directly dive into the content of cognitive robotics. To achieve this, the authors utilize containerization technologies and Kubernetes to deploy and operate containerized applications, including robotics simulation environments and software collections based on the Robot operating System (ROS). The web-based Integrated Development Environment JupyterLab is integrated with RvizWeb and XPRA to provide real-time visualization of sensor data and robot behavior in a user-friendly environment for interacting with robotics software. The paper also discusses the application of the platform in teaching Knowledge Representation, Reasoning, Acquisition and Retrieval, and Task-Executives. The authors conclude that the proposed platform is a valuable tool for education and research in cognitive robotics, and that it has the potential to democratize access to these fields. The platform has already been successfully employed in various academic courses, demonstrating its effectiveness in fostering knowledge and skill development.

Marlyse is a third-year PhD student in the Computer Science and Artificial Intelligence Laboratory at MIT. She received her B.S. in Aeronautics and Astronautics from MIT in 2017. Her current research in the Model-based Embedded and Robotic Systems Group focuses on multi-vehicle online planning, incorporating complex dynamics and constraints. She is also interested in risk-aware planning, fault protection and diagnosis, and adaptive sampling. Outside of the lab, she enjoys playing soccer, dancing, and reading science fiction.

For robots to successfully perceive and understand their environment, they must be taught to act in a goal-directed way. While mapping environments geometry is a necessary prerequisite for many mobile robot applications, understanding the semantics of the environment will enable novel applications, which require more advanced cognitive abilities. Sven Behnke, Head of Autonomous Intelligent Systems Group at the University of Bonn, is tackling this area of robotics by combining dense geometric modelling and semantic categorization. Through this, 3D semantic maps of the environment are built. Sven's team have demonstrated the utility of semantic environment perception with cognitive robots in multiple challenging application domains, including domestic service, space exploration, search and rescue, and bin picking.

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Artificial Intelligence (AI) is the form of intelligence that arises from machines, perceiving their environment and taking actions to maximize their chance of success for a given task. We live in a period where computer power is escalating quickly and where recent discoveries are bringing this power to the general public, often arising controversy. In this ecosystem, academia and industry define together a new paradigm to innovate and benefit humanity as a whole. Don't miss the opportunity to learn about AI in London, together with Imperial College London Innovation Forum (ICLIF). The event will be followed by a networking session with finger food and nibbles for the attendees.

A cognitive robot may face failures during the execution of its actions in the physical world. In this paper, we investigate how robots can ensure robustness by gaining experience on action executions, and we propose a lifelong experimental learning method. We use Inductive Logic Programming (ILP) as the learning method to frame new hypotheses. ILP provides first-order logic representations of the derived hypotheses that are useful for reasoning and planning processes. Furthermore, it can use background knowledge to represent more advanced rules. Partially specified world states can also be easily represented in these rules. All these advantages of ILP make this approach superior to attribute-based learning approaches. Experience gained through incremental learning is used as a guide to future decisions of the robot for robust execution. The results on our Pioneer 3DX robot reveal that the hypotheses framed for failure cases are sound and ensure safety in future tasks of the robot.

The Association for the Advancement of Artificial Intelligence (AAAI) presented the AAAI-02 Workshop Program on Sunday and Monday, 28-29 July 2002 at the Shaw Convention Center in Edmonton, Alberta, Canada. The AAAI-02 workshop program included 18 workshops covering a wide range of topics in AI. The workshops were Agent-Based Technologies for B2B Electronic-Commerce; Automation as a Caregiver: The Role of Intelligent Technology in Elder Care; Autonomy, Delegation, and Control: From Interagent to Groups; Coalition Formation in Dynamic Multiagent Environments; Cognitive Robotics; Game-Theoretic and Decision-Theoretic Agents; Intelligent Service Integration; Intelligent Situation-Aware Media and Presentations; Meaning Negotiation; Multiagent Modeling and Simulation of Economic Systems; Ontologies and the Semantic Web; Planning with and for Multiagent Systems; Preferences in AI and CP: Symbolic Approaches; Probabilistic Approaches in Search; Real-Time Decision Support and Diagnosis Systems; Semantic Web Meets Language Resources; and Spatial and Temporal Reasoning.