Online World Modeling Enables Real-World Inverse Reinforcement Learning from Observation
Online World Modeling Enables Real-World Inverse Reinforcement Learning from Observation
This research presents MPAIL2, the first method to learn visual manipulation in the real world from scratch using observational learning via Inverse Reinforcement Learning from Observation (IRLfO). It achieves 82% performance in pick-and-place tasks within 40 minutes, surpassing reinforcement learning and behavior cloning approaches with comparable interaction and demonstration budgets.
This paper introduces MPAIL2, a method that enables real-world visual manipulation learning from observation without reward or action supervision, achieving 82% performance in pick-and-place tasks within 40 minutes, outperforming reinforcement learning and behavior cloning approaches with similar interaction and demonstration budgets.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
interaction budget
0
View original evidence
ion tasks from scratch. In under 40 minutes, MPAIL2 learns pick-and-place from scratch to 82%, where RL and BC with equal interaction and demonstration budgets reach only 0% and 12% despite their reward and action supervision. Interactive project page with training videos: https://uwrobotlearning.github.io/mpail2/
Open source S4training time
<40 minutes
View original evidence
ion tasks from scratch. In under 40 minutes, MPAIL2 learns pick-and-place from scratch to 82%, where RL and BC with equal interaction and demonstration budgets reach only 0% and 12% despite their reward and action supervision. Interactive project page with training videos: https://uwrobotlearning.github.io/mpail2/
Open source S4
- code: not_reported
- paper: not_reported
- weights: not_reported
- dataset: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
ion tasks from scratch. In under 40 minutes, MPAIL2 learns pick-and-place from scratch to 82%, where RL and BC with equal interaction and demonstration budgets reach only 0% and 12% despite their reward and action supervision. Interactive project page with training videos: https://uwrobotlearning.github.io/mpail2/
Open source S4
Source：arXiv Robotics — research abstracts · arxiv.org