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Robot reinforcement learning on the constraint manifold
Reinforcement learning in robotics is extremely challenging due to many practical issues,
including safety, mechanical constraints, and wear and tear. Typically, these issues are not …
including safety, mechanical constraints, and wear and tear. Typically, these issues are not …
Fast kinodynamic planning on the constraint manifold with deep neural networks
Motion planning is a mature area of research in robotics with many well-established
methods based on optimization or sampling the state space, suitable for solving kinematic …
methods based on optimization or sampling the state space, suitable for solving kinematic …
A shared control framework for enhanced gras** performance in teleoperation
Remote teleoperation has shown significant advancements since the first teleoperation
system was proposed by Goertz in the 1940s. In recent years, the research on shared …
system was proposed by Goertz in the 1940s. In recent years, the research on shared …
Motion planning and inertia-based control for impact aware manipulation
In this article, we propose a metric called hitting flux, which is used in the motion generation
and controls for a robot manipulator to interact with the environment through a hitting or a …
and controls for a robot manipulator to interact with the environment through a hitting or a …
Safe reinforcement learning on the constraint manifold: Theory and applications
Integrating learning-based techniques, especially reinforcement learning, into robotics is
promising for solving complex problems in unstructured environments. However, most …
promising for solving complex problems in unstructured environments. However, most …
Robot air hockey: A manipulation testbed for robot learning with reinforcement learning
Reinforcement Learning is a promising tool for learning complex policies even in fast-
moving and object-interactive domains where human teleoperation or hard-coded policies …
moving and object-interactive domains where human teleoperation or hard-coded policies …
Bridging the gap between learning-to-plan, motion primitives and safe reinforcement learning
Trajectory planning under kinodynamic constraints is fundamental for advanced robotics
applications that require dexterous, reactive, and rapid skills in complex environments …
applications that require dexterous, reactive, and rapid skills in complex environments …
A Retrospective on the Robot Air Hockey Challenge: Benchmarking Robust, Reliable, and Safe Learning Techniques for Real-world Robotics
Abstract Machine learning methods have a groundbreaking impact in many application
domains, but their application on real robotic platforms is still limited. Despite the many …
domains, but their application on real robotic platforms is still limited. Despite the many …
DroPong: Enthusing Learners About Control Engineering by Revisiting the Pong Game with Aerial and Ground Drones*
This paper proposes an adapted version of the classic Pong game, tailored for educational
purposes and illustrated with two ground mobile robots and one drone, therefore called …
purposes and illustrated with two ground mobile robots and one drone, therefore called …
Energy-based Contact Planning under Uncertainty for Robot Air Hockey
Planning robot contact often requires reasoning over a horizon to anticipate outcomes,
making such planning problems computationally expensive. In this letter, we propose a …
making such planning problems computationally expensive. In this letter, we propose a …