Search Results for author:
Found 6 papers, 1 papers with code
Safe Reinforcement Learning on the Constraint Manifold: Theory and Applications
Videos of the real robot experiments are available on the project website (https://puzeliu. github. io/TRO-ATACOM).
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 motion planning.
Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interaction
Our proposed approach achieves state-of-the-art performance in simulated high-dimensional and dynamic tasks while avoiding collisions with the environment.
Dimensionality Reduction and Prioritized Exploration for Policy Search
Black-box policy optimization is a class of reinforcement learning algorithms that explores and updates the policies at the parameter level.
Regularized Deep Signed Distance Fields for Reactive Motion Generation
Autonomous robots should operate in real-world dynamic environments and collaborate with humans in tight spaces.
Composable Energy Policies for Reactive Motion Generation and Reinforcement Learning
Reactive motion generation problems are usually solved by computing actions as a sum of policies.
