10 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…
LEAP: Making Privileged Geometry Supervision Effective for Visuomotor Learning
Han Fang, Yunpeng Jiang, Jianshu Hu +6
Privileged 3D supervision uses additional geometric information during training to guide RGB-based visuomotor policy learning, without requiring geometric inputs at deployment. How…
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…
OMP: One-step Meanflow Policy with Directional Alignment
Han Fang, Yize Huang, Yuheng Zhao +3
Robot manipulation has increasingly adopted data-driven generative policy frameworks, yet the field faces a persistent trade-off: diffusion models suffer from high inference latenc…
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…
ASAP: Exploiting the Satisficing Generalization Edge in Neural Combinatorial Optimization
Han Fang, Paul Weng, Yutong Ban
Deep Reinforcement Learning (DRL) has emerged as a promising approach for solving Combinatorial Optimization (CO) problems, such as the 3D Bin Packing Problem (3D-BPP), Traveling S…
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…
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…