3 citations · 3 across the 8 of their papers we have counts for
11 papers
OneDemo: Unified Policy Learning and Data Generation from a Single Demonstration
Ying Qian, Jianshu Hu, Han Fang +2
Learning robot policies from demonstrations is often limited by the cost of collecting diverse, high-quality expert data. While an appealing alternative is to expand a single succe…
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…
ExtraGS: Enhancing Endoscopic View Extrapolation via Diffusion-Guided 3D Gaussian Splatting
Cheng-Tai Hsieh, Jiwei Shan, Han Fang +6
Robot-assisted minimally invasive surgery (MIS) critically depends on reliable endoscopic perception for navigation and safety. However, conventional endoscopes provide only a limi…
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…
SSP: Safety-guaranteed Surgical Policy via Joint Optimization of Behavioral and Spatial Constraints
Jianshu Hu, ZhiYuan Guan, Lei Song +5
The paradigm of robot-assisted surgery is shifting toward data-driven autonomy, where policies learned via Reinforcement Learning (RL) or Imitation Learning (IL) enable the executi…
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…
Diffusion Stabilizer Policy for Automated Surgical Robot Manipulations
Chonlam Ho, Jianshu Hu, Lei Song +3
Intelligent surgical robots have the potential to revolutionize clinical practice by enabling more precise and automated surgical procedures. However, the automation of such robot…
State-Novelty Guided Action Persistence in Deep Reinforcement Learning
Jianshu Hu, Paul Weng, Yutong Ban
While a powerful and promising approach, deep reinforcement learning (DRL) still suffers from sample inefficiency, which can be notably improved by resorting to more sophisticated…
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…