arXiv:2609.01596v1 Announce Type: cross
Abstract: Real-world robotic assembly at sub-millimeter tolerances demands spatial precision, compliant interaction, and robustness to contact failures. We pre...
By Haoyuan Deng, Haichao Liu, Wenkai Guo, Yuan Ling, Zaijia Yang, Yuanjiang Xue, Haosheng Sun, Liangzi Wang, Ziwei Wang
arXiv:2606. 27475v1 Announce Type: cross Abstract: Robots trained on real world data tend to be imprecise, slow, and brittle to perturbations.
By Raymond Yu, William Huey, Mustafa Mukadam, Anusha Nagabandi, Abhishek Gupta
FWBC‑VLA is a force‑aware framework that links vision‑language‑action (VLA) models with whole‑body compensation control for wheeled‑legged robots. It introduces HSR‑Force, a sensorless residual‑torque estimator that infers contact strength and encodes this information as tokens for the VLA action decoder, allowing the policy to detect contact onset, loading, and release. The system fine‑tunes a pretrained VLA backbone on a large WL&Arm dataset, combines proprioceptive, Jacobian‑derived force, and contact estimates to generate corrective actions, and demonstrates effectiveness in real‑world tasks such as whiteboard wiping and door opening.
By Yutian Zhang, Siyuan Ma, Liwen Yang, Yang Li, Ce Hao, Haozhen Chi, Dong We, Qiaojun Yu, Dibo Hou
The paper introduces ORPA, a framework that adds a lightweight, feedback-conditioned module to a pretrained robotic manipulation policy, enabling real‑time residual adjustments in joint space without retraining the base policy. ORPA allows immediate correction of execution errors and distribution shifts, improving success rates and recovery on precision‑sensitive tasks compared to baseline policies and rule‑based inverse kinematics. The method is evaluated on the ALOHA platform, showing its effectiveness in real‑time deployment scenarios.
By Muhammad A. Muttaqien, Tomohiro Motoda, Ryo Hanai, Yukiyasu Domae
The paper presents Real‑Time EXPO‑FT, a reinforcement learning framework that fine‑tunes large Vision‑Language‑Action models for real‑time robotic control. It separates slow, expressive action generation from fast, reactive edits, allowing a lightweight policy to adjust actions based on the latest observation. Experiments on the Kinetix benchmark and four dynamic real‑world tasks show that Real‑Time EXPO‑FT achieves superior performance, improving policy success rates from 42% to 97% with only ten minutes of online data and no human intervention.
By Perry Dong, Kuo-Han Hung, Dorsa Sadigh, Chelsea Finn
Real-world robotic assembly at sub-millimeter tolerances demands spatial precision, compliant interaction, and robustness to contact failures. We present Facet-0, a robotic foundation model that predi...