RoboClaw: An Agentic Framework for Scalable Long-Horizon Robotic Tasks
Vision-Language-Action (VLA) systems have shown strong potential for language-driven robotic manipulation. However, scaling them to long-horizon tasks remains challenging. Existing pipelines typically separate data collection, policy learning, and deployment, resulting in heavy reliance on manual environment resets and brittle multi-policy execution. We present RoboClaw, an agentic robotics framework that unifies data collection, policy learning, and task execution under a single VLM-driven controller. At the policy level, RoboClaw introduces Entangled Action Pairs (EAP), which couple forward manipulation behaviors with inverse recovery actions to form self-resetting loops for autonomous ...
| Authors | Ruiying Li, Yunlang Zhou, YuYao Zhu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 12 March 2026 |
| Last updated at source | 1 April 2026 |
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