11 citations · 18 across the 4 of their papers we have counts for
5 papers · 1 filter
ATK: Automatic Task-driven Keypoint Selection for Robust Policy Learning
Yunchu Zhang, Shubham Mittal, Zhengyu Zhang +3
Visuomotor policies often suffer from perceptual challenges, where visual differences between training and evaluation environments degrade policy performance. Policies relying on s…
Data Efficient Behavior Cloning for Fine Manipulation via Continuity-based Corrective Labels
Abhay Deshpande, Liyiming Ke, Quinn Pfeifer +2
We consider imitation learning with access only to expert demonstrations, whose real-world application is often limited by covariate shift due to compounding errors during executio…
Real World Offline Reinforcement Learning with Realistic Data Source
Gaoyue Zhou, Liyiming Ke, Siddhartha Srinivasa +3
Offline reinforcement learning (ORL) holds great promise for robot learning due to its ability to learn from arbitrary pre-generated experience. However, current ORL benchmarks are…
Grasping with Chopsticks: Combating Covariate Shift in Model-free Imitation Learning for Fine Manipulation
Liyiming Ke, Jingqiang Wang, Tapomayukh Bhattacharjee +2
Billions of people use chopsticks, a simple yet versatile tool, for fine manipulation of everyday objects. The small, curved, and slippery tips of chopsticks pose a challenge for p…
Telemanipulation with Chopsticks: Analyzing Human Factors in User Demonstrations
Liyiming Ke, Ajinkya Kamat, Jingqiang Wang +3
Chopsticks constitute a simple yet versatile tool that humans have used for thousands of years to perform a variety of challenging tasks ranging from food manipulation to surgery.…