CoRAL: Contact-Rich Adaptive LLM-based Control for Robotic Manipulation
While Large Language Models (LLMs) and Vision-Language Models (VLMs) demonstrate remarkable capabilities in high-level reasoning and semantic understanding, applying them directly to contact-rich manipulation remains a challenge due to their lack of explicit physical grounding and inability to perform adaptive control. To bridge this gap, we propose CoRAL (Contact-Rich Adaptive LLM-based control), a modular framework that enables zero-shot planning by decoupling high-level reasoning from low-level control. Unlike black-box policies, CoRAL uses LLMs not as direct controllers, but as cost designers that synthesize context-aware objective functions for a sampling-based motion planner (MPPI)....
| Authors | Berk Çiçek, Mert K. Er, Ozgur S. Oguz |
|---|---|
| Primary category | cs.RO |
| Published | 4 May 2026 |
| Last updated at source | 12 May 2026 |
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