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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 …
Handling Long-Term Safety and Uncertainty in Safe Reinforcement Learning
Safety is one of the key issues preventing the deployment of reinforcement learning
techniques in real-world robots. While most approaches in the Safe Reinforcement Learning …
techniques in real-world robots. While most approaches in the Safe Reinforcement Learning …
Spectral-Risk Safe Reinforcement Learning with Convergence Guarantees
The field of risk-constrained reinforcement learning (RCRL) has been developed to
effectively reduce the likelihood of worst-case scenarios by explicitly handling risk-measure …
effectively reduce the likelihood of worst-case scenarios by explicitly handling risk-measure …
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 …
ARMOR: Egocentric Perception for Humanoid Robot Collision Avoidance and Motion Planning
Humanoid robots have significant gaps in their sensing and perception, making it hard to
perform motion planning in dense environments. To address this, we introduce ARMOR, a …
perform motion planning in dense environments. To address this, we introduce ARMOR, a …
G-AlignNet: Geometry-Driven Quality Alignment for Robust Dynamical Systems Modeling
The Neural ODE family has shown promise in modeling complex systems but often assumes
consistent data quality, making them less effective in real-world applications with irregularly …
consistent data quality, making them less effective in real-world applications with irregularly …