机器人学会在关键时刻「多想一步」
机器人做长链条任务时，大多数模型每个动作只做一次前向推理，不管这一步多关键。这篇让机器人在决定「下一步做什么」时，能根据过往记忆和世界模型，在多个选项里搜索，直到找到更合理的子任务——相当于在关键时刻给自己加了个「慢思考」通道。它在4万小时异构数据上训练，跨场景测试中，多花计算量能显著提升子任务预测准确率，最终让长程操作成功率更高。它不是你明天能用上的，但指向一个趋势：机器人不再只是「快但傻」地执行，而是学会在重要节点上「慢但准」地决策。
📄 原文摘要(英文)
Long-horizon robot manipulation requires a robot to both execute individual skills reliably and sequence them coherently over extended tasks. Most hierarchical vision-language-action (VLA) models make each such decision with a single forward pass, leaving no mechanism to allocate additional computation to difficult or consequential choices. We introduce τ_0-VLA, a hierarchical robot foundation model that formulates high-level subtask generation as a compute-scalable inference problem through world-model-guided test-time computation. At each inference step, the high-level policy uses execution memory to generate a subtask and, when needed, searches over alternatives before committing to its output. A low-level policy then executes the generated subtask across multiple robot embodiments. The policy is trained on 40,115 hours of heterogeneous real-world data with multimodal co-training. Across in-domain and distribution-shifted settings, allocating additional test-time computation substantially improves next-subtask prediction accuracy, and these gains translate into higher closed-loop success on long-horizon robot manipulation tasks.