DetAug：用于零样本避障的障碍盲轨迹增强

中文摘要 AI 辅助

机器人操作策略是通过在大型遥操作数据集上训练而生成的。这些数据集通常由自由空间轨迹组成，这使得它们难以迁移到存在障碍物的测试环境中。先前弥合这一差距的方法主要分为两类。数据集增强在训练时解决该问题，但需要预先知道障碍物几何形状；而在推理时引导现有检查点则避免了这一要求，但灵活性有限。我们的方法汲取了两者的优点，而不继承任一缺点。DetAug 对自由空间数据集的过渡阶段应用一种障碍盲增强方案，不触碰物体交互部分，并将增强参数记录为显式条件标签。在推理时，它采样一批标签，并执行碰撞成本最低的轨迹。在 SafeLIBERO 基准上，DetAug 的无碰撞成功率比次优方法高出 20 个百分点以上，且在标签空间上的选择比在同一策略上的引导方法高出 26 个百分点。在真实硬件上，推理时引导方法在需要大绕行的任务上失效，而 DetAug 在训练中从未见过障碍物的情况下，匹配或超过了以障碍物为条件的基线。

英文摘要

Policies for robotic manipulation are produced by training on large teleoperated datasets. These datasets typically consist of free-space trajectories, making them difficult to transfer to test-time environments with obstacles. Previous methods for closing this gap have largely fallen into two groups. Dataset augmentation addresses it at training time but needs obstacle geometry in advance, whereas steering an existing checkpoint at inference time avoids that requirement but is limited in flexibility. Our method draws from both areas without inheriting either drawback. DetAug applies an obstacle-blind augmentation scheme to the transit phases of a free-space dataset, leaving object interactions untouched, and records the augmentation parameters as an explicit conditioning label. At inference it samples a batch of labels and executes the trajectory with the lowest collision cost. On the SafeLIBERO benchmark DetAug achieves a collision-free success rate more than 20pp above the next best method, and selecting over the label space outperforms guidance on the same policy by 26pp. On real hardware, inference-time steering methods collapse on tasks requiring large detours, while DetAug matches or exceeds an obstacle-conditioned baseline without ever seeing obstacles in training.

发表机构

• Oxford Robotics Institute, University of Oxford（牛津大学牛津机器人研究所）
• Purdue University（普渡大学）

机构由 AI 辅助整理，请以论文原文为准。