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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 …
Sampling constrained trajectories using composable diffusion models
Trajectory optimization and optimal control are powerful tools for synthesizing complex robot
behavior using appropriate cost functions and constraints. However, methods for solving the …
behavior using appropriate cost functions and constraints. However, methods for solving the …
Motion planning diffusion: Learning and adapting robot motion planning with diffusion models
The performance of optimization-based robot motion planning algorithms is highly
dependent on the initial solutions, commonly obtained by running a sampling-based planner …
dependent on the initial solutions, commonly obtained by running a sampling-based planner …
Pinsat: Parallelized interleaving of graph search and trajectory optimization for kinodynamic motion planning
Trajectory optimization is a widely used technique in robot motion planning for letting the
dynamics of the system shape and synthesize complex behaviors. Several previous works …
dynamics of the system shape and synthesize complex behaviors. Several previous works …
Physics-informed neural motion planning on constraint manifolds
Constrained Motion Planning (CMP) aims to find a collision-free path between the given
start and goal configurations on the kinematic constraint manifolds. These problems appear …
start and goal configurations on the kinematic constraint manifolds. These problems appear …
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 …
Trajectory manifold optimization for fast and adaptive kinodynamic motion planning
Fast kinodynamic motion planning is crucial for systems to effectively adapt to dynamically
changing environments. Despite some efforts, existing approaches still struggle with rapid …
changing environments. Despite some efforts, existing approaches still struggle with rapid …
Neural randomized planning for whole body robot motion
Robot motion planning has made vast advances over the past decades, but the challenge
remains: robot mobile manipulators struggle to plan long-range whole-body motion in …
remains: robot mobile manipulators struggle to plan long-range whole-body motion in …
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 …