37 citations · 76 across the 7 of their papers we have counts for
7 papers
Safe Reinforcement Learning of Autonomous Highway Driving: A Unified Framework for Safety and Efficiency
Chufei Yan, Zhihao Cui, Yiyan Lv +3
Deep reinforcement learning (DRL) offers a compelling route to decision-making for advanced autonomous vehicles (AVs), yet its trial-and-error nature makes it difficult to guarante…
QuadSwarm: A Modular Multi-Quadrotor Simulator for Deep Reinforcement Learning with Direct Thrust Control
Zhehui Huang, Sumeet Batra, Tao Chen +7
Reinforcement learning (RL) has shown promise in creating robust policies for robotics tasks. However, contemporary RL algorithms are data-hungry, often requiring billions of envir…
DexDeform: Dexterous Deformable Object Manipulation with Human Demonstrations and Differentiable Physics
Sizhe Li, Zhiao Huang, Tao Chen +4
In this work, we aim to learn dexterous manipulation of deformable objects using multi-fingered hands. Reinforcement learning approaches for dexterous rigid object manipulation wou…
HandoverSim: A Simulation Framework and Benchmark for Human-to-Robot Object Handovers
Yu-Wei Chao, Chris Paxton, Yu Xiang +6
We introduce a new simulation benchmark "HandoverSim" for human-to-robot object handovers. To simulate the giver's motion, we leverage a recent motion capture dataset of hand grasp…
Learning to Jump from Pixels
Gabriel B. Margolis, Tao Chen, Kartik Paigwar +4
Today's robotic quadruped systems can robustly walk over a diverse range of rough but continuous terrains, where the terrain elevation varies gradually. Locomotion on discontinuous…
Learning Exploration Policies for Navigation
Tao Chen, Saurabh Gupta, Abhinav Gupta
Numerous past works have tackled the problem of task-driven navigation. But, how to effectively explore a new environment to enable a variety of down-stream tasks has received much…