3 papers
cs.RO2026
EgoLive: A Large-Scale Egocentric Dataset from Real-World Human Tasks
Yihang Li, Xuelong Wei, Jingzhou Luo +26
The advancement of robot learning is currently hindered by the scarcity of large-scale, high-quality datasets. While established data collection methods such as teleoperation and u…
cs.RO2026
AtomicVLA: Unlocking the Potential of Atomic Skill Learning in Robots
Likui Zhang, Tao Tang, Zhihao Zhan +9
Recent advances in Visual-Language-Action (VLA) models have shown promising potential for robotic manipulation tasks. However, real-world robotic tasks often involve long-horizon,…
cs.CV2025
ActionSink: Toward Precise Robot Manipulation with Dynamic Integration of Action Flow
Shanshan Guo, Xiwen Liang, Junfan Lin +3
Language-instructed robot manipulation has garnered significant interest due to the potential of learning from collected data. While the challenges in high-level perception and pla…