DetAug: Obstacle-Blind Trajectory Augmentation for Zero-shot Obstacle Avoidance

2026-09-16

AI Summary

本文提出DetAug方法，通过障碍物盲增强自由空间数据集来解决机器人在未知障碍环境中避障问题，显著提高无碰撞成功率。

Abstract

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.

Problem

• obstacle avoidance
• dataset augmentation
• trajectory

Innovation

• obstacle-blind augmentation
• zero-shot obstacle avoidance
• trajectory augmentation
• explicit conditioning label