RoboEval is a new benchmark providing eight bimanual tasks, thousands of expert demonstrations, and standardized metrics for efficiency, coordination, safety, and failure localization in robotic manipulation.
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Augmenting robot datasets via diffusion-based semantic inpainting enables manipulation policies to solve unseen tasks with new objects and improves robustness to novel distractors.
RLFP and the FAC algorithm combine foundation-model priors for policy, value, and rewards to produce sample-efficient robotic RL that reaches 86% real-robot success after one hour and 100% success on 7/8 Meta-world tasks in under 100k frames.
citing papers explorer
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RoboEval: Where Robotic Manipulation Meets Structured and Scalable Evaluation
RoboEval is a new benchmark providing eight bimanual tasks, thousands of expert demonstrations, and standardized metrics for efficiency, coordination, safety, and failure localization in robotic manipulation.
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Scaling Robot Learning with Semantically Imagined Experience
Augmenting robot datasets via diffusion-based semantic inpainting enables manipulation policies to solve unseen tasks with new objects and improves robustness to novel distractors.
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Reinforcement Learning with Foundation Priors: Let the Embodied Agent Efficiently Learn on Its Own
RLFP and the FAC algorithm combine foundation-model priors for policy, value, and rewards to produce sample-efficient robotic RL that reaches 86% real-robot success after one hour and 100% success on 7/8 Meta-world tasks in under 100k frames.