4 citations · 6 across the 7 of their papers we have counts for
10 papers
Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables
Mengdi Xu, Peide Huang, Yaru Niu +8
One key challenge for multi-task Reinforcement learning (RL) in practice is the absence of task indicators. Robust RL has been applied to deal with task ambiguity, but may result i…
Learning Control Policies for Fall prevention and safety in bipedal locomotion
Visak Kumar
The ability to recover from an unexpected external perturbation is a fundamental motor skill in bipedal locomotion. An effective response includes the ability to not just recover b…
Modelling Human Kinetics and Kinematics during Walking using Reinforcement Learning
Visak Kumar
In this work, we develop an automated method to generate 3D human walking motion in simulation which is comparable to real-world human motion. At the core, our work leverages the a…
Error-Aware Policy Learning: Zero-Shot Generalization in Partially Observable Dynamic Environments
Visak Kumar, Sehoon Ha, C. Karen Liu
Simulation provides a safe and efficient way to generate useful data for learning complex robotic tasks. However, matching simulation and real-world dynamics can be quite challengi…
Joint Space Control via Deep Reinforcement Learning
Visak Kumar, David Hoeller, Balakumar Sundaralingam +2
The dominant way to control a robot manipulator uses hand-crafted differential equations leveraging some form of inverse kinematics / dynamics. We propose a simple, versatile joint…
Contextual Reinforcement Learning of Visuo-tactile Multi-fingered Grasping Policies
Visak Kumar, Tucker Hermans, Dieter Fox +2
Using simulation to train robot manipulation policies holds the promise of an almost unlimited amount of training data, generated safely out of harm's way. One of the key challenge…