RoboAware: Learning to Coordinate Embodied Skills from Count
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Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: RoboAware: Learning to Coordinate Embodied Skills from Counterfactual Outcomes. RoboAware is a new method for coordinating embodied skills in robots by learning from counterfactual outcomes. It uses a hierarchical MDP framework and introduces State-Locked Counterfactual Branching (SCB) and Execution…
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Reported quantity · tasks: 100 other · Basis not reported
“k. Comprehensive single-episode evaluations on 100 tasks show that RoboAware reaches a 77.0% overall success rate, with SOTA averages of 90.0% on RoboSuite, 73.8% on diverse LIBERO-Pro task clusters, and 90.0% on challenging RoboTwin bimanual tasks, outperforming existing code-as-policy and VLA-harness baselines.”
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- arXiv Robotics — research abstractsRoboAware: Learning to Coordinate Embodied Skills from Counterfactual Outcomes
RoboAware is a new method for coordinating embodied skills in robots by learning from counterfactual outcomes. It uses a hierarchical MDP framework and introduces State-Locked Counterfactual Branching (SCB) and Execution-Aware Learning (EAL) to improve policy selection. The method achieves a 77.0% overall success rate and outperforms existing baselines on several robotic benchmarks.
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