对抗性数据采集：人机协作扰动助力高效稳健的机器人模仿学习

Adversarial Data Collection: Human-Collaborative Perturbations for Efficient and Robust Robotic Imitation Learning

March 14, 2025

作者: Siyuan Huang, Yue Liao, Siyuan Feng, Shu Jiang, Si Liu, Hongsheng Li, Maoqing Yao, Guanghui Ren

cs.AI

摘要

在机器人操作领域，追求数据效率——即质量胜于数量——已成为一项基石，尤其是考虑到现实世界数据采集的高昂成本。我们提出，最大化单次演示的信息密度可以显著减少对大规模数据集的依赖，同时提升任务表现。为此，我们引入了对抗性数据采集（Adversarial Data Collection, ADC），一种人机交互（Human-in-the-Loop, HiL）框架，通过实时、双向的人与环境互动重新定义了机器人数据获取方式。与被动记录静态演示的传统流程不同，ADC采用了一种协作扰动范式：在一次任务过程中，对抗性操作者动态改变物体状态、环境条件和语言指令，而远程操作者则自适应调整动作以应对这些不断变化的挑战。这一过程将多样化的失败恢复行为、组合任务变化和环境扰动压缩至最少的演示中。我们的实验表明，经过ADC训练的模型在组合泛化到未见任务指令、增强对感知扰动的鲁棒性以及涌现错误恢复能力方面表现卓越。引人注目的是，仅使用通过ADC收集的20%演示量训练的模型，其性能显著优于使用完整数据集的传统方法。这些进展弥合了以数据为中心的学习范式与实际机器人部署之间的鸿沟，证明了战略性的数据采集，而不仅仅是事后处理，对于可扩展的现实世界机器人学习至关重要。此外，我们正在策划一个大规模的ADC-机器人数据集，包含带有对抗性扰动的现实世界操作任务。这一基准将开源，以促进机器人模仿学习的进步。

English

The pursuit of data efficiency, where quality outweighs quantity, has emerged as a cornerstone in robotic manipulation, especially given the high costs associated with real-world data collection. We propose that maximizing the informational density of individual demonstrations can dramatically reduce reliance on large-scale datasets while improving task performance. To this end, we introduce Adversarial Data Collection, a Human-in-the-Loop (HiL) framework that redefines robotic data acquisition through real-time, bidirectional human-environment interactions. Unlike conventional pipelines that passively record static demonstrations, ADC adopts a collaborative perturbation paradigm: during a single episode, an adversarial operator dynamically alters object states, environmental conditions, and linguistic commands, while the tele-operator adaptively adjusts actions to overcome these evolving challenges. This process compresses diverse failure-recovery behaviors, compositional task variations, and environmental perturbations into minimal demonstrations. Our experiments demonstrate that ADC-trained models achieve superior compositional generalization to unseen task instructions, enhanced robustness to perceptual perturbations, and emergent error recovery capabilities. Strikingly, models trained with merely 20% of the demonstration volume collected through ADC significantly outperform traditional approaches using full datasets. These advances bridge the gap between data-centric learning paradigms and practical robotic deployment, demonstrating that strategic data acquisition, not merely post-hoc processing, is critical for scalable, real-world robot learning. Additionally, we are curating a large-scale ADC-Robotics dataset comprising real-world manipulation tasks with adversarial perturbations. This benchmark will be open-sourced to facilitate advancements in robotic imitation learning.