Failure-Aware Bimanual Teleoperation via Conservative Value Guided Assistance
Peng Zhou, Zhongxuan Li, Jinsong Wu, Jiaming Qi, Jun Hu, David Navarro-Alarcon, Jia Pan, Lihua Xie, Shiyao Zhang, and Zeqing Zhang

TL;DR
This paper introduces a conservative value-guided assistance framework for bimanual teleoperation that enhances task success and safety by estimating failure risk from offline data and providing adaptive haptic guidance.
Contribution
It presents a novel failure-aware teleoperation method using Conservative Value Learning to model task feasibility and regulate assistance without overriding human control.
Findings
Improved task success rates in contact-rich manipulation tasks.
Reduced operator workload compared to baseline methods.
Effective embedding of failure awareness into teleoperation control.
Abstract
Teleoperation of high-precision manipulation is con-strained by tight success tolerances and complex contact dy-namics, which make impending failures difficult for human operators to anticipate under partial observability. This paper proposes a value-guided, failure-aware framework for bimanual teleoperation that provides compliant haptic assistance while pre-serving continuous human authority. The framework is trained entirely from heterogeneous offline teleoperation data containing both successful and failed executions. Task feasibility is mod-eled as a conservative success score learned via Conservative Value Learning, yielding a risk-sensitive estimate that remains reliable under distribution shift. During online operation, the learned success score regulates the level of assistance, while a learned actor provides a corrective motion direction. Both are integrated through a…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTeleoperation and Haptic Systems · Robot Manipulation and Learning · Tactile and Sensory Interactions
