Publikation
CompliantVLA-adaptor: VLM-Guided Variable Impedance Action for Safe Contact-Rich Manipulation
Heng Zhang; Wei-Hsing Huang; Qiyi Tong; Gökhan Solak; Puze Liu; Jimmy Dao Sheng Liu; Jan Peters; Arash Ajoudani
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2601.15541, Pages 1-9, airXiv, 2026.
Zusammenfassung
We propose a CompliantVLA-adaptor that aug-
ments the state-of-the-art Vision-Language-Action (VLA) mod-
els with vision-language model (VLM)-informed context-aware
variable impedance control (VIC) to improve the safety and
effectiveness of contact-rich robotic manipulation tasks. Existing
VLA systems (e.g., RDT, Pi0.5, OpenVLA-oft) typically output
position, but lack force-aware adaptation, leading to unsafe or
failed interactions in physical tasks involving contact, compliance,
or uncertainty. In the proposed CompliantVLA-adaptor, a VLM
interprets task context from images and natural language to
adapt the stiffness and damping parameters of a VIC con-
troller. These parameters are further regulated using real-time
force/torque feedback to ensure interaction forces remain within
safe thresholds. We demonstrate that our method outperforms
the VLA baselines on a suite of complex contact-rich tasks, both
in simulation and the real world, with improved success rates
and reduced force violations. This work presents a promising
path towards a safe foundation model for physical contact-rich
manipulation. We release our code, prompts, and force-torque-
impedance-scenario context datasets at https://sites.google.com/
view/compliantvla.