82 citations · 132 across the 38 of their papers we have counts for
16 papers · 1 filter
Ego4WAM: What Matters When Scaling Egocentric Human Data for Robot Learning?
Zhihao Sun, Liu Liu, Xinjiang Wang +6
Egocentric human data provides a scalable source of experience for robot learning, but varies substantially in human-robot alignment, behavioral coverage, and available supervision…
HandEdit: A Unified Benchmark for Egocentric Human-to-Robot Dexterous Hand Image Editing
Zhenjie Yang, Xingyu Jiao, Guopeng Zhong +18
Robotic manipulation with dexterous hands is a cornerstone of Embodied AI, yet its progress is stifled by the high cost of collecting embodiment-aware teleoperation data. While abu…
EgoRecovery: Acquiring Failure Recovery Ability Through Human Recovery Demonstration
Zuhao Ge, Yuchen Zhou, Weitao Zhou +8
Robust embodied robots should be able to recover from failures and retry tasks in order to operate reliably in unstructured and noisy real-world environments. Achieving this capabi…
ReactSim-Bench: Benchmarking Reactive Behavior World Model Simulation in Autonomous Driving
Zhiyuan Zhang, Yanlun Peng, Jianing Zhang +7
Reactive capability is a key property of data-driven behavior world model simulators for autonomous driving simulation systems. With this capability, simulated world agents can res…
UniDexTok: A Unified Dexterous Hand Tokenizer from Real Data
Dong Fang, Youjun Wu, Yuanxin Zhong +4
Dexterous hands are essential for fine-grained manipulation, but their hardware designs vary substantially across embodiments. Differences in kinematics, joint definitions, and deg…
GuidedVLA: Specifying Task-Relevant Factors via Plug-and-Play Action Attention Specialization
Xiaosong Jia, Bowen Yang, Zuhao Ge +17
Vision-Language-Action (VLA) models aim for general robot learning by aligning action as a modality within powerful Vision-Language Models (VLMs). Existing VLAs rely on end-to-end…
Bench2Drive-Robust: Benchmarking Closed-Loop Autonomous Driving under Deployment Perturbations
Zhiyuan Zhang, Zhenghao Jin, Yanlun Peng +8
Robustness is a critical requirement for deploying autonomous driving systems in the real world. Existing robustness benchmarks for autonomous driving have made important progress…
Bench2Drive-VL: Benchmarks for Closed-Loop Autonomous Driving with Vision-Language Models
Xiaosong Jia, Yuqian Shao, Zhenjie Yang +3
With the rise of vision-language models (VLM), their application for autonomous driving (VLM4AD) has gained significant attention. Meanwhile, in autonomous driving, closed-loop eva…
Can Users Specify Driving Speed? Bench2Drive-Speed: Benchmark and Baselines for Desired-Speed Conditioned Autonomous Driving
Yuqian Shao, Xiaosong Jia, Langechuan Liu +1
End-to-end autonomous driving (E2E-AD) has achieved remarkable progress. However, one practical and useful function has been long overlooked: users may wish to customize the desire…
ACE-Brain-0: Spatial Intelligence as a Shared Scaffold for Universal Embodiments
Ziyang Gong, Zehang Luo, Anke Tang +21
Universal embodied intelligence demands robust generalization across heterogeneous embodiments, such as autonomous driving, robotics, and unmanned aerial vehicles (UAVs). However,…
Raw2Drive: Reinforcement Learning with Aligned World Models for End-to-End Autonomous Driving (in CARLA v2)
Zhenjie Yang, Xiaosong Jia, Qifeng Li +3
Reinforcement Learning (RL) can mitigate the causal confusion and distribution shift inherent to imitation learning (IL). However, applying RL to end-to-end autonomous driving (E2E…
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…
Bench2Drive-R: Turning Real World Data into Reactive Closed-Loop Autonomous Driving Benchmark by Generative Model
Junqi You, Xiaosong Jia, Zhiyuan Zhang +2
For end-to-end autonomous driving (E2E-AD), the evaluation system remains an open problem. Existing closed-loop evaluation protocols usually rely on simulators like CARLA being les…
Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving
Xiaosong Jia, Zhenjie Yang, Qifeng Li +2
In an era marked by the rapid scaling of foundation models, autonomous driving technologies are approaching a transformative threshold where end-to-end autonomous driving (E2E-AD)…
Think2Drive: Efficient Reinforcement Learning by Thinking in Latent World Model for Quasi-Realistic Autonomous Driving (in CARLA-v2)
Qifeng Li, Xiaosong Jia, Shaobo Wang +1
Real-world autonomous driving (AD) especially urban driving involves many corner cases. The lately released AD simulator CARLA v2 adds 39 common events in the driving scene, and pr…
DriveAdapter: Breaking the Coupling Barrier of Perception and Planning in End-to-End Autonomous Driving
Xiaosong Jia, Yulu Gao, Li Chen +3
End-to-end autonomous driving aims to build a fully differentiable system that takes raw sensor data as inputs and directly outputs the planned trajectory or control signals of the…