BeyondContact: A Reinforcement Learning Framework for Airflow-Based Non-Contact Manipulation
Overview
This research introduces BeyondContact, a robot- and task-agnostic airflow extension for Isaac Lab that enables reinforcement learning for non-contact manipulation. The framework combines contact-based pushing with airflow manipulation (HPB policy) and uses airflow alone for reorientation (RR policy). A lightweight, high-speed blower was designed to coexist with a gripper. In zero-shot real-world tests, the hybrid push-blow policy achieved an 80% success rate over 20 trials, while the rectangular reorientation policy achieved a mean absolute heading error of 2.6 degrees across four target headings. The project materials are available on GitHub.
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Reported quantity · Reported success rate: 80 percent · reported trials · Reported zero-shot result; the excerpt does not specify an evaluation protocol.
“policy that combines contact-based pushing with airflow manipulation and a rectangular reorientation (RR) policy that controls a cuboid's heading using airflow alone. To enable physical execution, we design a lightweight, high-speed blower that coexists with a gripper. In zero-shot real-world deployment, HPB achieves an 80% success rate over 20 trials, while RR achieves a mean absolute heading error of 2.6 degrees a”
Exact source · revision 1Source owner not reported
Reported quantity · heading error: 2.6 other · Basis not reported
“policy that combines contact-based pushing with airflow manipulation and a rectangular reorientation (RR) policy that controls a cuboid's heading using airflow alone. To enable physical execution, we design a lightweight, high-speed blower that coexists with a gripper. In zero-shot real-world deployment, HPB achieves an 80% success rate over 20 trials, while RR achieves a mean absolute heading error of 2.6 degrees a”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalBeyondContact: A Reinforcement Learning Framework for Airflow-Based Non-Contact Manipulation
This research introduces BeyondContact, a robot- and task-agnostic airflow extension for Isaac Lab that enables reinforcement learning for non-contact manipulation. The framework combines contact-based pushing with airflow manipulation (HPB policy) and uses airflow alone for reorientation (RR policy). A lightweight, high-speed blower was designed to coexist with a gripper. In zero-shot real-world tests, the hybrid push-blow policy achieved an 80% success rate over 20 trials, while the rectangular reorientation policy achieved a mean absolute heading error of 2.6 degrees across four target headings. The project materials are available on GitHub.
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