6 papers
Direction Matters: Learning Force Direction Enables Sim-to-Real Contact-Rich Manipulation
Yifei Yang, Anzhe Chen, Zhenjie Zhu +6
Sim-to-real transfer for contact-rich manipulation remains challenging due to the inherent discrepancy in contact dynamics. While existing methods often rely on costly real-world d…
Mr. Virgil: Learning Multi-robot Visual-range Relative Localization
Si Wang, Zhehan Li, Jiadong Lu +3
Ultra-wideband (UWB)-vision fusion localization has achieved extensive applications in the domain of multi-agent relative localization. The challenging matching problem between rob…
High-Precision and High-Efficiency Trajectory Tracking for Excavators Based on Closed-Loop Dynamics
Ziqing Zou, Cong Wang, Yue Hu +6
The complex nonlinear dynamics of hydraulic excavators, such as time delays and control coupling, pose significant challenges to achieving high-precision trajectory tracking. Tradi…
Toward Embodiment Equivariant Vision-Language-Action Policy
Anzhe Chen, Yifei Yang, Zhenjie Zhu +4
Vision-language-action policies learn manipulation skills across tasks, environments and embodiments through large-scale pre-training. However, their ability to generalize to novel…
Disambiguate Gripper State in Grasp-Based Tasks: Pseudo-Tactile as Feedback Enables Pure Simulation Learning
Yifei Yang, Lu Chen, Zherui Song +5
Grasp-based manipulation tasks are fundamental to robots interacting with their environments, yet gripper state ambiguity significantly reduces the robustness of imitation learning…
CNSv2: Probabilistic Correspondence Encoded Neural Image Servo
Anzhe Chen, Hongxiang Yu, Shuxin Li +5
Visual servo based on traditional image matching methods often requires accurate keypoint correspondence for high precision control. However, keypoint detection or matching tends t…