Jim Fan团队搞了个新东西叫ASPIRE，让机器人自己攒技能库，迁移学习快了10倍，还开源了全栈代码。
Jim Fan团队发布ASPIRE，这是他们物理自研系列的第二项工作。ASPIRE让机器人构建一个自我进化的技能库，解决第100个任务时不再像第一个任务那样茫然。该方法通过编码代理观察模拟和真实机器人的多模态传感轨迹，对控制程序进行进化搜索，并将最佳知识提炼到不断扩展的库中。ASPIRE实现了约10倍的迁移学习token削减，相比传统从头重训练效率大幅提升。项目展示了超过90项技能和150+任务，代码将开源。
ENPIRE -> ASPIRE, our 2nd work in the series for Physical AutoResearch. We are building the compo...
ENPIRE -> ASPIRE, our 2nd work in the series for Physical AutoResearch. We are building the components for robot self-improvement, one /skill at a time. Jim Fan @DrJimFan Today, we give robots a /skills library that self-evolves and compounds indefinitely! Introducing ASPIRE: a robot solving its 100th task is no longer as clueless as solving its first. Coding agents observe multimodal sensory traces from simulation and real robots, launch an evolutionary search over control programs, and distill the best know-how into an ever-expanding library. ASPIRE is a new type of continual learning: "training" is skill refinement instead of gradient descent. "Trained model" is a repo of sensorimotor skills instead of floating weights. “Distributed training” is a panel of agents each practicing a different skill instead of sharded minibatches. Here's the beauty: ASPIRE gives the tired terms "sim2real transfer" and "cross-embodiment transfer" a whole new meaning. Bridging the sim-to-real gap is notoriously brutal. An end-to-end policy has to swallow both the visual shift (sim looks toyish next to a real camera) and the subtle contact physics it never quite gets right. ASPIRE sidesteps the mess, because it doesn't ship pixels or weights across the gap, but ships the know-how. The robot still has to practice in the real world, not zero-shot, but it gets there way faster because it isn't rediscovering the strategy from scratch. Same for going single-arm to bimanual hardware, which usually requires new data and retraining from zero. ASPIRE achieves up to ~10x cut in "transfer learning” tokens (yes, tokens are the new unit of *training* compute ;) Check out our gallery of 150+ tasks and 90+ skills the robots taught themselves, all on the website! Kind of wild that we can ship the "learned weights" as an HTML page rather than a GGUF. We'll open-source the full stack so your own robot library starts compounding from ours! Deep dive in thread: Your browser does not support the video tag. 🔗 View on Twitter 🔗 View Quoted Tweet 💬 5 🔄 1 ❤️ 47 👀 5643 📊 9 ⚡