103 citations · 188 across the 27 of their papers we have counts for
17 papers · 1 filter
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…
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…
GeoManip: Geometric Constraints as General Interfaces for Robot Manipulation
Weiliang Tang, Jia-Hui Pan, Yun-Hui Liu +4
We present GeoManip, a framework to enable generalist robots to leverage essential conditions derived from object and part relationships, as geometric constraints, for robot manipu…
DexH2R: Task-oriented Dexterous Manipulation from Human to Robots
Shuqi Zhao, Xinghao Zhu, Yuxin Chen +5
Dexterous manipulation is a critical aspect of human capability, enabling interaction with a wide variety of objects. Recent advancements in learning from human demonstrations and…
Language-Driven Policy Distillation for Cooperative Driving in Multi-Agent Reinforcement Learning
Jiaqi Liu, Chengkai Xu, Peng Hang +4
The cooperative driving technology of Connected and Autonomous Vehicles (CAVs) is crucial for improving the efficiency and safety of transportation systems. Learning-based methods,…
Embodiment-Agnostic Action Planning via Object-Part Scene Flow
Weiliang Tang, Jia-Hui Pan, Wei Zhan +6
Observing that the key for robotic action planning is to understand the target-object motion when its associated part is manipulated by the end effector, we propose to generate the…
Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven Decision-Making Framework
Shiyu Fang, Jiaqi Liu, Mingyu Ding +4
At present, Connected Autonomous Vehicles (CAVs) have begun to open road testing around the world, but their safety and efficiency performance in complex scenarios is still not sat…
P2 Explore: Efficient Exploration in Unknown Cluttered Environment with Floor Plan Prediction
Kun Song, Gaoming Chen, Masayoshi Tomizuka +3
Robot exploration aims at the reconstruction of unknown environments, and it is important to achieve it with shorter paths. Traditional methods focus on optimizing the visiting ord…
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…
DrPlanner: Diagnosis and Repair of Motion Planners for Automated Vehicles Using Large Language Models
Yuanfei Lin, Chenran Li, Mingyu Ding +3
Motion planners are essential for the safe operation of automated vehicles across various scenarios. However, no motion planning algorithm has achieved perfection in the literature…
PhyGrasp: Generalizing Robotic Grasping with Physics-informed Large Multimodal Models
Dingkun Guo, Yuqi Xiang, Shuqi Zhao +4
Robotic grasping is a fundamental aspect of robot functionality, defining how robots interact with objects. Despite substantial progress, its generalizability to counter-intuitive…
SkillDiffuser: Interpretable Hierarchical Planning via Skill Abstractions in Diffusion-Based Task Execution
Zhixuan Liang, Yao Mu, Hengbo Ma +3
Diffusion models have demonstrated strong potential for robotic trajectory planning. However, generating coherent trajectories from high-level instructions remains challenging, esp…
Human-oriented Representation Learning for Robotic Manipulation
Mingxiao Huo, Mingyu Ding, Chenfeng Xu +6
Humans inherently possess generalizable visual representations that empower them to efficiently explore and interact with the environments in manipulation tasks. We advocate that s…
Generalizable Long-Horizon Manipulations with Large Language Models
Haoyu Zhou, Mingyu Ding, Weikun Peng +3
This work introduces a framework harnessing the capabilities of Large Language Models (LLMs) to generate primitive task conditions for generalizable long-horizon manipulations with…
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…
LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving
Hao Sha, Yao Mu, Yuxuan Jiang +7
Existing learning-based autonomous driving (AD) systems face challenges in comprehending high-level information, generalizing to rare events, and providing interpretability. To add…