title: Adversarial Data Collection: Human-Collaborative Perturbations for
    Efficient and Robust Robotic Imitation Learning
summary: 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.
paper_authors._id: 67d78c194fd0e3fa3a082f8d
paper_authors.name: Siyuan Huang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-17T08:44:21.620Z
paper_authors._id: 634e4120038b5879133552f5
paper_authors.avatarUrl: /avatars/34ec861b4bbf1aecf927a7d6e726c7a4.svg
paper_authors.fullname: Siyuan
paper_authors.type: user
paper_authors.user: SiyuanH
paper_authors._id: 67d78c194fd0e3fa3a082f8e
paper_authors.name: Yue Liao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-17T08:49:27.927Z
paper_authors._id: 670f827bb94a3734d270f707
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/D6qCPBMJAUgozfG7YTwky.png
paper_authors.fullname: Yue Liao
paper_authors.type: user
paper_authors.user: morninghaze
paper_authors._id: 67d78c194fd0e3fa3a082f8f
paper_authors.name: Siyuan Feng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-17T08:44:19.837Z
paper_authors._id: 620326e962b2b0e46e79971b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/620326e962b2b0e46e79971b/1FVPRpsWng5q3An4qbuYQ.jpeg
paper_authors.fullname: Siyuan Feng
paper_authors.type: user
paper_authors.user: Eralien
paper_authors._id: 67d78c194fd0e3fa3a082f90
paper_authors.name: Shu Jiang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-17T14:42:21.098Z
paper_authors._id: 666c463389e21df7d4a34d03
paper_authors.avatarUrl: /avatars/8f73e78d740ace263961de1a2896fc09.svg
paper_authors.fullname: 姜姝
paper_authors.type: user
paper_authors.user: jiang12345
paper_authors._id: 67d78c194fd0e3fa3a082f91
paper_authors.name: Si Liu
paper_authors._id: 67d78c194fd0e3fa3a082f92
paper_authors.name: Hongsheng Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-17T08:49:51.674Z
paper_authors._id: 65c04e9c27a5fdca81abcbd9
paper_authors.avatarUrl: /avatars/12a155683c824fa23da4a9e2bed4f64e.svg
paper_authors.fullname: Hongsheng LI
paper_authors.type: user
paper_authors.user: hsli-cuhk
paper_authors._id: 67d78c194fd0e3fa3a082f93
paper_authors.name: Maoqing Yao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-17T08:49:59.798Z
paper_authors._id: 67739bfa64e8b7438ae68eb4
paper_authors.avatarUrl: /avatars/15193bfbce487b2de4ce8c86bd18885a.svg
paper_authors.fullname: Maoqing Yao
paper_authors.type: user
paper_authors.user: AutobotZero
paper_authors._id: 67d78c194fd0e3fa3a082f94
paper_authors.name: Guanghui Ren
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-17T08:50:05.432Z
paper_authors._id: 646ec9b135f55eb49e405faa
paper_authors.avatarUrl: /avatars/a17194be585d20e2a021e77a5a20e213.svg
paper_authors.fullname: Guanghui Ren
paper_authors.type: user
paper_authors.user: sundrops
paper_title: Adversarial Data Collection: Human-Collaborative Perturbations for
    Efficient and Robust Robotic Imitation Learning
paper_summary: 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.
paper_ai_summary: Adversarial Data Collection, a Human-in-the-Loop framework that uses dynamic perturbations, reduces data needs while enhancing the performance and robustness of robotic manipulation models.
paper_ai_keywords: Adversarial Data Collection Human-in-the-Loop HiL bidirectional interactions adversarial operator tele-operator failure-recovery behaviors compositional task variations perceptual perturbations error recovery capabilities robotic manipulation imitation learning