Evo-RL: Iterative Robot Reinforcement Learning from Selective Experience
To this end, we propose Evo-RL, an iterative reinforcement learning method that combines human interventions with advantage-guided experience selection.
ProofPaper ↗
Key points
- Existing real-world reinforcement learning methods improve robot policies by combining human corrections with autonomous rollouts.
- 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 high-precision manipulation task, Evo-RL improves attempt success rate over RECAP by 30.46%.
Sources (1)
- [1]Evo-RL: Iterative Robot Reinforcement Learning from Selective ExperiencearXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 10:19 AM
To this end, we propose Evo-RL, an iterative reinforcement learning method that combines human interventions with advantage-guided experience selection.
Existing real-world reinforcement learning methods improve robot policies by combining human corrections with autonomous rollouts.
Extractive summary: sentences quoted from the sources.