14 papers
4D Visual Pre-training for Robot Learning
Chengkai Hou, Yanjie Ze, Yankai Fu +5
General visual representations learned from web-scale datasets for robotics have achieved great success in recent years, enabling data-efficient robot learning on manipulation task…
A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control
Zilin Kang, Chenyuan Hu, Yu Luo +3
Deep reinforcement learning for continuous control has recently achieved impressive progress. However, existing methods often suffer from primacy bias, a tendency to overfit early…
Make-An-Agent: A Generalizable Policy Network Generator with Behavior-Prompted Diffusion
Yongyuan Liang, Tingqiang Xu, Kaizhe Hu +3
Can we generate a control policy for an agent using just one demonstration of desired behaviors as a prompt, as effortlessly as creating an image from a textual description? In thi…
DOGlove: Dexterous Manipulation with a Low-Cost Open-Source Haptic Force Feedback Glove
Han Zhang, Songbo Hu, Zhecheng Yuan +1
Dexterous hand teleoperation plays a pivotal role in enabling robots to achieve human-level manipulation dexterity. However, current teleoperation systems often rely on expensive e…
RoboDuet: Learning a Cooperative Policy for Whole-body Legged Loco-Manipulation
Guoping Pan, Qingwei Ben, Zhecheng Yuan +6
Fully leveraging the loco-manipulation capabilities of a quadruped robot equipped with a robotic arm is non-trivial, as it requires controlling all degrees of freedom (DoFs) of the…
Unleashing the Power of Pre-trained Language Models for Offline Reinforcement Learning
Ruizhe Shi, Yuyao Liu, Yanjie Ze +2
Offline reinforcement learning (RL) aims to find a near-optimal policy using pre-collected datasets. In real-world scenarios, data collection could be costly and risky; therefore,…