Search Results for author:
Found 9 papers, 3 papers with code
Learning Human-to-Humanoid Real-Time Whole-Body Teleoperation
We present Human to Humanoid (H2O), a reinforcement learning (RL) based framework that enables real-time whole-body teleoperation of a full-sized humanoid robot with only an RGB camera.
Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion
Legged robots navigating cluttered environments must be jointly agile for efficient task execution and safe to avoid collisions with obstacles or humans.
Safe Deep Policy Adaptation
In contrast, policy adaptation based on reinforcement learning (RL) offers versatility and generalizability but presents safety and robustness challenges.
State-wise Safe Reinforcement Learning: A Survey
Despite the tremendous success of Reinforcement Learning (RL) algorithms in simulation environments, applying RL to real-world applications still faces many challenges.
Visual Imitation Learning with Patch Rewards
Specifically, we present Adversarial Imitation Learning with Patch Rewards (PatchAIL), which employs a patch-based discriminator to measure the expertise of different local parts from given images and provide patch rewards.
AutoCost: Evolving Intrinsic Cost for Zero-violation Reinforcement Learning
Results show that the converged policies with intrinsic costs in all environments achieve zero constraint violation and comparable performance with baselines.
Reinforcement Learning with Automated Auxiliary Loss Search
A good state representation is crucial to solving complicated reinforcement learning (RL) challenges.
AARL: Automated Auxiliary Loss for Reinforcement Learning
A good state representation is crucial to reinforcement learning (RL) while an ideal representation is hard to learn only with signals from the RL objective.
Energy-Based Imitation Learning
We tackle a common scenario in imitation learning (IL), where agents try to recover the optimal policy from expert demonstrations without further access to the expert or environment reward signals.
