35 citations · 54 across the 10 of their papers we have counts for
10 papers
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…
Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL
Andrew Wagenmaker, Kevin Huang, Liyiming Ke +3
In order to mitigate the sample complexity of real-world reinforcement learning, common practice is to first train a policy in a simulator where samples are cheap, and then deploy…
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…
CCIL: Continuity-based Data Augmentation for Corrective Imitation Learning
Liyiming Ke, Yunchu Zhang, Abhay Deshpande +2
We present a new technique to enhance the robustness of imitation learning methods by generating corrective data to account for compounding errors and disturbances. While existing…
Cherry-Picking with Reinforcement Learning : Robust Dynamic Grasping in Unstable Conditions
Yunchu Zhang, Liyiming Ke, Abhay Deshpande +2
Grasping small objects surrounded by unstable or non-rigid material plays a crucial role in applications such as surgery, harvesting, construction, disaster recovery, and assisted…
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.…
Imitation Learning as -Divergence Minimization
Liyiming Ke, Sanjiban Choudhury, Matt Barnes +3
We address the problem of imitation learning with multi-modal demonstrations. Instead of attempting to learn all modes, we argue that in many tasks it is sufficient to imitate any…
Tactical Rewind: Self-Correction via Backtracking in Vision-and-Language Navigation
Liyiming Ke, Xiujun Li, Yonatan Bisk +6
We present the Frontier Aware Search with backTracking (FAST) Navigator, a general framework for action decoding, that achieves state-of-the-art results on the Room-to-Room (R2R) V…