Jim Fan团队用RoboTTT让机器人记住5分钟的动作，上下文拉到8000步，性能飙升62%，还能从视频里学新活。
Jim Fan团队推出RoboTTT，将机器人模型上下文长度从不到0.1秒扩展到5分钟（8000步），且推理成本恒定。与现有方法相比，性能提升3个数量级。RoboTTT基于Test-Time Training（TTT），在模型内部携带一个小型核心，每帧触发梯度更新，将历史压缩到权重中。8K上下文预训练比1K性能高出62%，且无饱和迹象。该方法支持从人类视频进行一次性上下文学习，并能在部署后持续自我改进，机器人可实时从错误中恢复。
How it's done: https://t.co/DanOfnw4xk
How it's done: x.com/DrJimFan/statu… Jim Fan @DrJimFan 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 🔗 View Quoted Tweet 💬 0 🔄 0 ❤️ 2 👀 340 ⚡