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Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables
One key challenge for multi-task Reinforcement learning (RL) in practice is the absence of task indicators.
Learning Control Policies for Fall prevention and safety in bipedal locomotion
For robots associated with bipedal locomotion, such as humanoid robots and assistive robotic devices that aid humans in walking, designing controllers which can provide this stability and safety can prevent damage to robots or prevent injury related medical costs.
Modelling Human Kinetics and Kinematics during Walking using Reinforcement Learning
In this work, we develop an automated method to generate 3D human walking motion in simulation which is comparable to real-world human motion.
Error-Aware Policy Learning: Zero-Shot Generalization in Partially Observable Dynamic Environments
An EAP takes as input the predicted future state error in the target environment, which is provided by an error-prediction function, simultaneously trained with the EAP.
Contextual Reinforcement Learning of Visuo-tactile Multi-fingered Grasping Policies
We propose a Grasping Objects Approach for Tactile (GOAT) robotic hands, learning to overcome the reality gap problem.
Robotics
