机器人模型终于能记住5分钟内的动作了！RoboTTT用TTT方法让机器人从人类视频一次学会新任务，还能边干边自我纠错，性能随上下文增长持续提升。
研究团队推出RoboTTT模型，通过测试时训练（TTT）机制，将机器人策略的上下文窗口从不足0.1秒扩展到8000时间步（约5分钟），推理成本保持恒定。该模型在上下文长度上比现有SOTA提升了3个数量级。实验显示，从128到8000时间步，闭环性能持续提升，8K预训练比1K提升62%。RoboTTT支持从人类视频进行一次性上下文学习，并能在执行过程中自我纠正错误。
We scaled a robot model natively to 8,000 timesteps of context, 5 minutes worth of muscle memory, wi...
We scaled a robot model natively to 8,000 timesteps of context, 5 minutes worth of muscle memory, with constant inference cost. Robot policies used to live their lives a few frames at a time (< 0.1 sec), instantly forgetting what just happened. We pushed to 3 orders of magnitude beyond SOTA. Introducing RoboTTT. Test-Time Training (“TTT”) carries a tiny model *inside* the model. Every incoming sensor reading triggers one gradient step on that tiny core, so the history keeps getting compressed into its weights. The hidden state has a fixed size (literally a small neural net), so the robot can “grok” arbitrarily long experience with little overhead. Learning continues indefinitely after deployment. We can then put an entire video in context as prompt! RoboTTT enables one-shot in-context learning from human video: in circuit board assembly, a human demonstrates a never-seen configuration once, and the robot imitates it faithfully. Humans drop things all the time, but we pick them up so fast that we don’t even notice. That reflex to fix is half of our physical competence. RoboTTT shows self-improvement on the fly: the robot is skilled at recovering from its own errors mid-episode, and each fix enters its context to inform the next move. The TTT core distills a general-purpose, failure-to-correction mapping from the training data. One more thing. What excites me the most is a new Context Scaling Curve: from 128 to 8K timesteps, closed-loop performance hill-climbs steadily with no sign of saturation. 8K-context pretraining beats 1K by 62%. What LLM enjoys, robotics should too. Soon, even 1M context is not a fantasy. Deep dive in thread: Your browser does not support the video tag. 🔗 View on Twitter 💬 9 🔄 5 ❤️ 29 👀 4263 📊 15 ⚡