外观
论文arXiv 2610.2179813 天前
从人类视频到机器人操作的跨具身因果迁移
原文链接：Cross-Embodiment Causal Transfer from Human Videos to Robot Manipulation（arXiv 2610.21798 · 发布日期：2026-10-15）
Cross-Embodiment Causal Transfer from Human Videos to Robot Manipulation
针对人类视频难以直接监督机器人动作的问题，CBST以因果束选择语义条件，在机器人操作基准验证成功率提升。
核心要点
- 动机是机器人操作数据采集昂贵，而人类操作视频丰富但具身差异大。
- 方法从因果束视角分析子任务迁移，提出CBST选择应保留的上下文与交互依赖。
- CBST用人类视频学习操作表征，并借助机器人示范将其落地到任务级语义变量。
- 实验在机器人操作基准上，相比对应world-action模型基线提升任务成功率。
验证范围与局限：验证范围为机器人操作基准，摘要未说明仿真或真机等细节。
英文摘要 (Abstract)
Scaling robot manipulation models requires large amounts of interaction data, yet collecting diverse real-robot experience remains costly. Human manipulation videos, in contrast, provide abundant and diverse task experience that could potentially benefit robot learning. However, differences in morphology, action spaces, and control mechanisms make human motions unsuitable as direct supervision for robot actions, raising a scientific question: what knowledge in human videos should be transferred across embodiments to robot manipulation? To address this question, we analyze subtask transfer through the lens of causal beams, focusing on which contextual conditions and interaction dependencies should be retained to support local task effects. Based on this perspective, we propose Causal-Beam-Guided Subtask Transfer (CBST). CBST learns manipulation-related representations from human videos and grounds them in task-level semantic variables using robot demonstrations. Within a predefined manipulation model, causal-beam constraints guide contextual information selection by requiring retained assignments to sustain local mechanism outputs while preserving nontrivial responsiveness to retained inputs. The selected semantic information conditions a world-action model, allowing the robot to generate actions from its own observations without requiring human-robot motion correspondence. Experiments on robot manipulation benchmarks show improvements in task success over the corresponding world-action model baselines.
作者：Xingyu Zhang, 李映超, Peizheng Guo, Jie Hu, Changwen Zheng, Wenwen Qiang
来源
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