[Submitted on 22 Oct 2026]
Title:FORCA: A Force-Aware Coding Agent for Contact-Rich Robot Manipulation
View a PDF of the paper titled FORCA: A Force-Aware Coding Agent for Contact-Rich Robot Manipulation, by Xiao Tan and 3 other authors
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Abstract:Coding agents can translate language instructions into robot programs. For contact-rich manipulation, a key challenge is to generate and refine control logic that responds to physical interaction. We present FORCA, a coding agent that takes visual observations and wrist force/torque (F/T) measurements as input to write Python programs for contact probing and force-responsive motion. Between executions, the agent inspects force records alongside visual outcomes to diagnose failures and revise both motion and force-feedback control logic. FORCA requires no demonstrations, task-specific training, or simulation. Across five real-robot tasks, we demonstrate its effectiveness in three forms of contact-rich manipulation: improving manipulation accuracy, adapting to changes in object or surface properties, and regulating contact under surface variation and object motion. Compared with a vision-only variant of the same agent, FORCA improves grasp accuracy and task success and reduces contact loss in wiping and peeling.
Comments: Project website: this https URL
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2610.31841 [cs.RO]
(or arXiv:2610.31841v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.31841
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yixuan Wang [view email] [v1] Thu, 22 Oct 2026 19:24:48 UTC (9,074 KB)
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View a PDF of the paper titled FORCA: A Force-Aware Coding Agent for Contact-Rich Robot Manipulation, by Xiao Tan and 3 other authors
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