14 citations · 15 across the 9 of their papers we have counts for
9 papers
Bridge3D: Enabling Vision-Language-Action Models to See and Act in 3D
Haoxuan Li, Sixu Yan, Lianghui Zhu +3
Vision-Language-Action (VLA) models have demonstrated remarkable generalization in robotic manipulation via large-scale multimodal pretraining. However, VLA models are mainly train…
Adaptive Vision-Language Grasping via Composable Foundation Priors and Generalizable Grasp Synthesis
Sixu Yan, Shikang Wang, Binhua Huang +11
This paper proposes AdaRoboVLG, a task-adaptive Vision-Language-Grasp (VLG) framework that supports generalizable grasp synthesis across different robotic hands. Unlike existing VL…
UniDriveVLA: Unifying Understanding, Perception, and Action Planning for Autonomous Driving
Yongkang Li, Lijun Zhou, Sixu Yan +11
Vision-Language-Action (VLA) models have recently emerged in autonomous driving, with the promise of leveraging rich world knowledge to improve the cognitive capabilities of drivin…
OmniTrack: General Motion Tracking via Physics-Consistent Reference
Yuhan Li, Peiyuan Zhi, Yunshen Wang +6
Learning motion tracking from rich human motion data is a foundational task for achieving general control in humanoid robots, enabling them to perform diverse behaviors. However, d…
ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving
Yongkang Li, Kaixin Xiong, Xiangyu Guo +12
Recent studies have explored leveraging the world knowledge and cognitive capabilities of Vision-Language Models (VLMs) to address the long-tail problem in end-to-end autonomous dr…
Towards Fast, Memory-based and Data-Efficient Vision-Language Policy
Haoxuan Li, Sixu Yan, Yuhan Li +1
Vision Language Models (VLMs) pretrained on Internet-scale vision-language data have demonstrated the potential to transfer their knowledge to robotic learning. However, the existi…
DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving
Bencheng Liao, Shaoyu Chen, Haoran Yin +8
Recently, the diffusion model has emerged as a powerful generative technique for robotic policy learning, capable of modeling multi-mode action distributions. Leveraging its capabi…
M2Diffuser: Diffusion-based Trajectory Optimization for Mobile Manipulation in 3D Scenes
Sixu Yan, Zeyu Zhang, Muzhi Han +7
Recent advances in diffusion models have opened new avenues for research into embodied AI agents and robotics. Despite significant achievements in complex robotic locomotion and sk…
M3Bench: Benchmarking Whole-body Motion Generation for Mobile Manipulation in 3D Scenes
Zeyu Zhang, Sixu Yan, Muzhi Han +4
We propose M3Bench, a new benchmark for whole-body motion generation in mobile manipulation tasks. Given a 3D scene context, M3Bench requires an embodied agent to reason about its…