MIT、Stanford 等机构合作发布机器人物理递归自改进综述，提出六级框架
一篇由 MIT、Stanford、Google、Microsoft 等多家机构研究者合作的综述论文，系统梳理了机器人物理递归自改进研究。论文将自改进划分为 L0 到 L5 六个等级，L0 为无自主改进，L5 为经独立验证的递归增益，中间等级区分对技能本身的更新与对改进方法本身的更新，以及改进发生在单一任务还是跨任务。
Very insightful survey paper from, written by many of the top labs including MIT, Stanford, Google, Microsoft, for Physical Recursive Self-Improvement
Robots still rely on people to collect data, diagnose failures, and choose which updates to keep. Argues that 5 of 7 robot self-improvement studies they checked never showed gains on a different task.
Self-improvement claims need tests on tasks that require different skill sequences, and finds most reviewed protocols lack them.
Humans still collect their data, diagnose failures, and pick which updates to keep. Real trials are slow, noisy, costly, and sometimes irreversible, so digital self-improvement methods need rethinking.
They define 6 levels, from L0 with no autonomous improvement to L5 with independently verified recursive gain. The middle levels separate updates to skills from updates to the improvement method, on 1 task or across tasks.
Their map of systems from 2022 to 2026 has no confirmed L5 case. Of 7 robot evaluation protocols they examined, 5 did not show improvement across tasks.
来源：rohanpaul_ai · x.com