3 citations · 3 across the 6 of their papers we have counts for
8 papers
RESTA: Time-Scaling Augmentation for Data-Efficient Robot Imitation Learning
Yunpeng Jiang, Jianshu Hu, Han Fang +3
Robot manipulation policies trained through imitation learning commonly rely on costly demonstrations recorded in one task direction and at one temporal resolution. We propose Reve…
Policy-as-Data: Learning Generalizable HOI Diffusion Models from Simulated Physics
Shujia Li, Jianshu Hu, Haiyu Zhang +5
Synthesizing realistic Human-Object Interactions (HOI) is critical for creating embodied avatars and functional virtual environments. However, current data-driven approaches primar…
DSSP: Diffusion State Space Policy with Full-History Encoding
Zhiyuan Guan, Jianshu Hu, Han Fang +5
Diffusion-based imitation learning has shown strong promise for robot manipulation. However, most existing policies condition only on the current observation or a short window of r…
Generalizable Coarse-to-Fine Robot Manipulation via Language-Aligned 3D Keypoints
Jianshu Hu, Lidi Wang, Shujia Li +4
Hierarchical coarse-to-fine policy, where a coarse branch predicts a region of interest to guide a fine-grained action predictor, has demonstrated significant potential in robotic…
Time Reversal Symmetry for Efficient Robotic Manipulations in Deep Reinforcement Learning
Yunpeng Jiang, Jianshu Hu, Paul Weng +1
Symmetry is pervasive in robotics and has been widely exploited to improve sample efficiency in deep reinforcement learning (DRL). However, existing approaches primarily focus on s…
Robot Collapse: Supply Chain Backdoor Attacks Against VLM-based Robotic Manipulation
Xianlong Wang, Hewen Pan, Hangtao Zhang +8
Robotic manipulation policies are increasingly empowered by \textit{large language models} (LLMs) and \textit{vision-language models} (VLMs), leveraging their understanding and per…
Understanding and Reducing the Class-Dependent Effects of Data Augmentation with A Two-Player Game Approach
Yunpeng Jiang, Yutong Ban, Paul Weng
Data augmentation is widely applied and has shown its benefits in different machine learning tasks. However, as recently observed, it may have an unfair effect in multi-class class…
Revisiting Data Augmentation in Deep Reinforcement Learning
Jianshu Hu, Yunpeng Jiang, Paul Weng
Various data augmentation techniques have been recently proposed in image-based deep reinforcement learning (DRL). Although they empirically demonstrate the effectiveness of data a…