3 citations · 5 across the 8 of their papers we have counts for
11 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…
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…
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…
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…
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…
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…
INViT: A Generalizable Routing Problem Solver with Invariant Nested View Transformer
Han Fang, Zhihao Song, Paul Weng +1
Recently, deep reinforcement learning has shown promising results for learning fast heuristics to solve routing problems. Meanwhile, most of the solvers suffer from generalizing to…
Unsupervised Salient Patch Selection for Data-Efficient Reinforcement Learning
Zhaohui Jiang, Paul Weng
To improve the sample efficiency of vision-based deep reinforcement learning (RL), we propose a novel method, called SPIRL, to automatically extract important patches from input im…