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论文arXiv 2610.0117127 天前

具身不匹配下基于经验的可行性感知从观察生成对抗模仿 ​

针对人机具身与动力学差异导致示范不可行，EF-GAIfO用机器人自身经验估计可行性，并在仿真行走和真机四足物体到达与抓取任务验证。

AI 整理
  • 本体 SDK 与控制
  • 仿真与训练
  • 数据与采集

原文链接：Experience-Based Feasibility-Aware Generative Adversarial Imitation from Observation under Embodiment Mismatch（arXiv 2610.01171 · 发布日期：2026-10-01）

Experience-Based Feasibility-Aware Generative Adversarial Imitation from Observation under Embodiment Mismatch

针对人机具身与动力学差异导致示范不可行，EF-GAIfO用机器人自身经验估计可行性，并在仿真行走和真机四足物体到达与抓取任务验证。

核心要点 ​

  • 无机器人示范接口提供无动作标签的状态轨迹，但人类动作因具身和动力学差异可能对机器人不可行。
  • EF-GAIfO从机器人自身经验估计状态示范可行性，不依赖显式动力学模型或大型先验探索数据集。
  • 可行性随策略学习动态演化，策略改进后经历更广状态转移，逐步扩展可行区域并纳入更多示范。
  • 方法在仿真运动任务和真实四足机器人物体到达与抓取任务中验证了有效性。

验证范围与局限：验证范围为仿真中的运动任务和真实四足机器人的物体到达与抓取任务。

英文摘要 (Abstract) ​

With the increasing use of robot-free demonstration interfaces that provide state trajectories without action labels, imitation from observation has become a promising approach for learning robot behaviors from human demonstrations. However, due to differences in embodiment and dynamics between humans and robots, demonstrated human motions may not be feasible for the robot, potentially degrading policy performance. In this study, we propose Experience-Based Feasibility-Aware Generative Adversarial Imitation from Observation (EF-GAIfO), which estimates the feasibility of state-only demonstrations from the robot's own experience rather than relying on explicit dynamics models or large prior exploration datasets. A key feature of EF-GAIfO is that the notion of feasibility evolves with policy learning: as the policy improves and the robot experiences a broader range of state transitions, the feasible region is progressively expanded, allowing additional demonstrations to be incorporated into learning. This enables feasibility-aware imitation that adapts to the current stage of policy learning, rather than relying on a pre-designed feasibility criterion. We validate the effectiveness of EF-GAIfO on a locomotion task in simulation and on a real quadruped robot performing a object-reaching-and-grasping task.

作者：Yoshiki Takebayashi, Giovanni Perantoni, Hikaru Sasaki, Matteo Saveriano, Takamitsu Matsubara

来源

本页事实对应的公开页面，以原文为准。

  1. arXiv 2610.01171：Experience-Based Feasibility-Aware Generative Adversarial Imitation from Observation under Embodiment Mismatch访问 2026-10-21