探索RSI，生数新世界模型让机器人开始自我进化

TL;DR - ShengShu Technology introduced Motus2, a multimodal world-action model that enables robots to act, predict outcomes, evaluate results, and improve their policies in a closed loop. It is an early exploration of recursive self-improvement for embodied agents, not unrestricted 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.
• Model-based reinforcement learning converts those scores into policy updates while freezing prediction and evaluation components. Planning plus policy learning raised average success on two real-robot tasks from 65% to 75%.
• Tactile input improved average success on cup extraction and paper tearing from 60% to 72.5%, while retained observation history supported tasks requiring memory.
• Training uses roughly 130,000 hours of first-person human video plus robot and human-robot alignment data; robot-domain intermediate training increased five-task average success from 51% to 84%.