2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 2 cited
REBOOT: Reuse Data for Bootstrapping Efficient Real-World Dexterous Manipulation
Zheyuan Hu, Aaron Rovinsky, Jianlan Luo +3
Dexterous manipulation tasks involving contact-rich interactions pose a significant challenge for both model-based control systems and imitation learning algorithms. The complexity…
cs.LG2022
Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidance
Kelvin Xu, Zheyuan Hu, Ria Doshi +4
Complex and contact-rich robotic manipulation tasks, particularly those that involve multi-fingered hands and underactuated object manipulation, present a significant challenge to…
cs.LG2021
Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention
Abhishek Gupta, Justin Yu, Tony Z. Zhao +5
Reinforcement Learning (RL) algorithms can in principle acquire complex robotic skills by learning from large amounts of data in the real world, collected via trial and error. Howe…