Evo-RL: Iterative Robot Reinforcement Learning from Selective Experience
Overview
Evo-RL integrates human corrections with experience selection to boost robot task success by 30.46%. Dataset and code are publicly available.,
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Event evidence and corrections
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Reported quantity · Reported duration: 1000 hours · Basis not reported
“ive reinforcement learning method that combines human interventions with advantage-guided experience selection. Evo-RL uses human corrections and experience with positive estimated advantages from successful trajectories to iteratively refine the policy. To support learning from these different experience sources, we introduce RW-RL-Dataset, which spans over 1,000 hours across four robot embodiments. On a real-world”
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Reported quantity · robot embodiments: 4 robots · Basis not reported
“ive reinforcement learning method that combines human interventions with advantage-guided experience selection. Evo-RL uses human corrections and experience with positive estimated advantages from successful trajectories to iteratively refine the policy. To support learning from these different experience sources, we introduce RW-RL-Dataset, which spans over 1,000 hours across four robot embodiments. On a real-world”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsEvo-RL: Iterative Robot Reinforcement Learning from Selective Experience
Evo-RL is an iterative reinforcement learning method that integrates human corrections with advantage-guided experience selection to refine robot policies. The approach leverages experience from successful trajectories to improve policy performance, achieving a 30.46% increase in task success rate compared to RECAP on a high-precision manipulation task. The RW-RL-Dataset, spanning over 1,000 hours across four robot embodiments, is made publicly available along with code and model checkpoints.
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