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<p>Learning an effective manipulation policy with high efficiency in robotics continues to be a significant challenge. In this paper, we propose E-GAIL, which aims to learn manipulation policies efficiently from a limited set of demonstrations with negative corruption and long-term rewards under the framework of GAIL. Specifically, we propose two techniques: 1) Utilizing both short-term and long-term observations to offer additional rewards for training, accelerating convergence. 2) Incorporating negative actions into generated trajectories for corruption to improve data effectiveness and increase success rates. E-GAIL achieves a 25% improvement in success rates across multiple manipulation tasks, requiring 70% fewer episodes for policy convergence, highlighting its efficiency with limited demonstrations. Our video is available at <a href="https://youtu.be/bIDfOjYcY54">https://youtu.be/bIDfOjYcY54</a>.</p>

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E-GAIL: efficient GAIL through including negative corruption and long-term rewards for robotic manipulations

  • 期刊论文

  • 发表日期：

    2025年4月7日

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  • Jiayi Tan,
  • Gang Chen,
  • Zeyuan Huang,
  • Haofeng Liu,
  • Marcelo H. Ang Jr

摘要

Learning an effective manipulation policy with high efficiency in robotics continues to be a significant challenge. In this paper, we propose E-GAIL, which aims to learn manipulation policies efficiently from a limited set of demonstrations with negative corruption and long-term rewards under the framework of GAIL. Specifically, we propose two techniques: 1) Utilizing both short-term and long-term observations to offer additional rewards for training, accelerating convergence. 2) Incorporating negative actions into generated trajectories for corruption to improve data effectiveness and increase success rates. E-GAIL achieves a 25% improvement in success rates across multiple manipulation tasks, requiring 70% fewer episodes for policy convergence, highlighting its efficiency with limited demonstrations. Our video is available at https://youtu.be/bIDfOjYcY54.