AgenticRoboRL: Bringing Coding-Agent Post-Training to Agentic Robotics
Agentic robotics systems combine planners with robot harnesses to perform complex manipulation tasks. Recent work improves these systems through better harnesses and external memory, but relies on powerful closed-source models as planners and does not improve the planners' own capabilities. In contrast, coding agents have improved substantially by post-training the model itself. We therefore investigate whether post-training can also improve robot planners. However, applying it across robot harnesses requires accommodating different execution mechanisms, including VLA skill invocation and control-code generation. To this end, we introduce AgenticRoboRL, a framework for post-training robot pl
| Authors | Xinxin Liu, Bingxin Xu, Wenrui Bao et al. |
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| Published | 20 October 2026 |
| Last updated at source | — |
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