探索RSI，生数新世界模型让机器人开始自我进化
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Merged summary
TL;DR - ShengShu Technology unveiled Motus2, a multimodal world-action model that lets robots generate actions, predict outcomes, evaluate results, and improve their policies through a closed feedback loop. It marks an early, bounded exploration of recursive self-improvement for robotic manipulation rather than open-ended autonomous learning.
- Motus2 combines action generation, an action-conditioned world model, and a value model; Best-of-N planning simulates and scores candidate actions before execution.
- Planning plus model-based reinforcement learning raised average success on two real-robot tasks from 65% to 75%.
- Adding tactile feedback improved paper-tearing and cup-extraction success from 60% to 72.5%, while observation memory supported tasks requiring historical context.
- Training uses roughly 130,000 hours of human egocentric video plus robot-alignment data; robot-domain intermediate training raised five-task average success from 51% to 84%.
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探索RSI，生数新世界模型让机器人开始自我进化
TL;DR - ShengShu Technology unveiled Motus2, a multimodal world-action model that lets robots generate actions, predict outcomes, evaluate results, and improve their policies through a closed feedback loop. It marks an early, bounded exploration of recursive self-improvement for robotic manipulation rather than open-ended autonomous learning.
- Motus2 combines action generation, an action-conditioned world model, and a value model; Best-of-N planning simulates and scores candidate actions before execution.
- Planning plus model-based reinforcement learning raised average success on two real-robot tasks from 65% to 75%.
- Adding tactile feedback improved paper-tearing and cup-extraction success from 60% to 72.5%, while observation memory supported tasks requiring historical context.
- Training uses roughly 130,000 hours of human egocentric video plus robot-alignment data; robot-domain intermediate training raised five-task average success from 51% to 84%.