2 citations · 4 across the 15 of their papers we have counts for
16 papers
CARF: Contrastive Attraction-Repulsion of Failure-Guided Flow Matching
Shuqi Zhao, Bang Du, Cheng-En Wu +3
Robot demonstration collection often produces imperfect or failed trajectories in addition to successful demonstrations. Existing methods typically exploit failed trajectories by i…
Diagnosing Compositional Generalization in Sequential Robot Tasks
Yixiao Wang, Cheng-En Wu, Lingfeng Sun +5
Sequential robot manipulation requires policies to execute novel combinations of familiar instruction components. However, collecting demonstrations for all possible instruction tu…
Multi-Camera View Scaling for Data-Efficient Robot Imitation Learning
Yichen Xie, Yixiao Wang, Shuqi Zhao +4
The generalization ability of imitation learning policies for robotic manipulation is fundamentally constrained by the diversity of expert demonstrations, while collecting demonstr…
Mean Flow Policy with Instantaneous Velocity Constraint for One-step Action Generation
Guojian Zhan, Letian Tao, Pengcheng Wang +6
Learning expressive and efficient policy functions is a promising direction in reinforcement learning (RL). While flow-based policies have recently proven effective in modeling com…
DADP: Domain Adaptive Diffusion Policy
Pengcheng Wang, Qinghang Liu, Haotian Lin +4
Learning domain adaptive policies that can generalize to unseen transition dynamics, remains a fundamental challenge in learning-based control. Substantial progress has been made t…
VER: Vision Expert Transformer for Robot Learning via Foundation Distillation and Dynamic Routing
Yixiao Wang, Mingxiao Huo, Zhixuan Liang +8
Pretrained vision foundation models (VFMs) advance robotic learning via rich visual representations, yet individual VFMs typically excel only in specific domains, limiting generali…
Chain-of-Thought for Autonomous Driving: A Comprehensive Survey and Future Prospects
Yixin Cui, Haotian Lin, Shuo Yang +3
The rapid evolution of large language models in natural language processing has substantially elevated their semantic understanding and logical reasoning capabilities. Such profici…
Interleave-VLA: Enhancing Robot Manipulation with Interleaved Image-Text Instructions
Cunxin Fan, Xiaosong Jia, Yihang Sun +8
The rise of foundation models paves the way for generalist robot policies in the physical world. Existing methods relying on text-only instructions often struggle to generalize to…
Physics-Aware Robotic Palletization with Online Masking Inference
Tianqi Zhang, Zheng Wu, Yuxin Chen +6
The efficient planning of stacking boxes, especially in the online setting where the sequence of item arrivals is unpredictable, remains a critical challenge in modern warehouse an…
Imagined Potential Games: A Framework for Simulating, Learning and Evaluating Interactive Behaviors
Lingfeng Sun, Yixiao Wang, Pin-Yun Hung +4
Interacting with human agents in complex scenarios presents a significant challenge for robotic navigation, particularly in environments that necessitate both collision avoidance a…
DexHandDiff: Interaction-aware Diffusion Planning for Adaptive Dexterous Manipulation
Zhixuan Liang, Yao Mu, Yixiao Wang +6
Dexterous manipulation with contact-rich interactions is crucial for advanced robotics. While recent diffusion-based planning approaches show promise for simple manipulation tasks,…
DeSiRe-GS: 4D Street Gaussians for Static-Dynamic Decomposition and Surface Reconstruction for Urban Driving Scenes
Chensheng Peng, Chengwei Zhang, Yixiao Wang +6
We present DeSiRe-GS, a self-supervised gaussian splatting representation, enabling effective static-dynamic decomposition and high-fidelity surface reconstruction in complex drivi…
Optimizing Diffusion Models for Joint Trajectory Prediction and Controllable Generation
Yixiao Wang, Chen Tang, Lingfeng Sun +8
Diffusion models are promising for joint trajectory prediction and controllable generation in autonomous driving, but they face challenges of inefficient inference steps and high c…
Sparse Diffusion Policy: A Sparse, Reusable, and Flexible Policy for Robot Learning
Yixiao Wang, Yifei Zhang, Mingxiao Huo +8
The increasing complexity of tasks in robotics demands efficient strategies for multitask and continual learning. Traditional models typically rely on a universal policy for all ta…
Composition Vision-Language Understanding via Segment and Depth Anything Model
Mingxiao Huo, Pengliang Ji, Haotian Lin +3
We introduce a pioneering unified library that leverages depth anything, segment anything models to augment neural comprehension in language-vision model zero-shot understanding. T…
Joint Pedestrian Trajectory Prediction through Posterior Sampling
Haotian Lin, Yixiao Wang, Mingxiao Huo +3
Joint pedestrian trajectory prediction has long grappled with the inherent unpredictability of human behaviors. Recent investigations employing variants of conditional diffusion mo…