2 citations · 2 across the 5 of their papers we have counts for
5 papers
LAPP: Large Language Model Feedback for Preference-Driven Reinforcement Learning
Pingcheng Jian, Xiao Wei, Yanbaihui Liu +3
We introduce Large Language Model-Assisted Preference Prediction (LAPP), a novel framework for robot learning that enables efficient, customizable, and expressive behavior acquisit…
Perception Stitching: Zero-Shot Perception Encoder Transfer for Visuomotor Robot Policies
Pingcheng Jian, Easop Lee, Zachary Bell +2
Vision-based imitation learning has shown promising capabilities of endowing robots with various motion skills given visual observation. However, current visuomotor policies fail t…
Policy Stitching: Learning Transferable Robot Policies
Pingcheng Jian, Easop Lee, Zachary Bell +2
Training robots with reinforcement learning (RL) typically involves heavy interactions with the environment, and the acquired skills are often sensitive to changes in task environm…
DAIR: Disentangled Attention Intrinsic Regularization for Safe and Efficient Bimanual Manipulation
Minghao Zhang, Pingcheng Jian, Yi Wu +2
We address the problem of safely solving complex bimanual robot manipulation tasks with sparse rewards. Such challenging tasks can be decomposed into sub-tasks that are accomplisha…
Adversarial Skill Learning for Robust Manipulation
Pingcheng Jian, Chao Yang, Di Guo +2
Deep reinforcement learning has made significant progress in robotic manipulation tasks and it works well in the ideal disturbance-free environment. However, in a real-world enviro…