Reproduce “BeyondContact: A Reinforcement Learning Framework for Airflow-Based Non-Contact Manipulation” on OpenResearch

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About this paper

Peiyu Yang, Mohamad S. Moudallal, Antonio Langella, Giulio Turrisi, Victor Barasuol, Claudio Semini

BeyondContact adds experimentally identified airflow models to Isaac Lab and trains policies that combine pushing with blower forces for objects beyond direct reach. It models jet spread and applies surface-based forces and torques in simulation. In zero-shot hardware tests, the cube entered the target in 16/20 trials, while cuboid heading error averaged 2.6° across four tested targets. Results demonstrate these tasks only.

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