MPAIL2: Online World Modeling for Real-World IRLfO
Overview
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.
From arXiv Robotics — research abstracts
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · interaction budget: 0 other · Basis not reported
“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/”
Exact source · revision 1Source owner not reported
Reported quantity · training time: 40 other · Basis not reported
“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/”
Exact source · revision 1Source owner not reported
Evidence dependency changes (3)
- 2026-10-30T22:16:45.342Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
- 2026-10-30T22:16:29.851Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
- 2026-10-21T13:26:21.495Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
Report timeline
Follow attributed reports and material updates.
- arXiv Robotics — research abstractsSignalOnline 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.
Timezone · UTC
Article dates follow your selected timezone. Briefing editions use Hong Kong time (UTC+8).