RoboAware: Learning to Coordinate Embodied Skills from Counterfactual Outcomes
Embodied coding agents can combine modular robot skills with frozen end-to-end policies, yet effective composition requires anticipating which policy family will succeed in the current physical state. We present RoboAware, which builds on coding agents' skill orchestration by learning only a state-conditioned responsibility coordinator from counterfactual outcomes. Inspired by the success of REPL, we propose the $P^5$ schema and formulate a hierarchical MDP based on it. $P^5$ organizes skills uniformly into five semantic stages, defining where responsibility can be compared. To address the lack of counterfactual branch outcomes in existing work, we introduce State-Locked Counterfactual Br...
| Authors | Bohan Zhou, Xingbei Chen, Emily Huang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 8 October 2026 |
| Last updated at source | 8 October 2026 |
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