学习接触丰富的全身操纵与示例引导的强化学习
Jose A Barreiros1, Aykut Özgün Önol1, Mengchao Zhang1
1Toyota Research Institute, Cambridge, MA, USA.
Science robotics
|August 20, 2025
概括
机器人现在可以使用示例引导强化学习来学习大物体的全身操纵技巧. 这种方法使机器人能够使用触觉和自身感知反来执行复杂的任务, 即使没有视觉跟踪.
科学领域:
- 机器人技术
- 人工智能
- 人与机器人的互动
背景情况:
- 人类操纵涉及复杂的全身运动和皮肤/肌肉遵守稳定性.
- 由于组合的复杂性,机器人操作面临着接触丰富的场景的挑战.
- 现有的方法对于广泛的机器人对象交互有很大的困难.
研究的目的:
- 通过示例引导的强化学习来培养机器人的全身操纵能力.
- 解决机器人操纵中接触丰富行为的挑战.
- 让机器人能够有效地操纵大而重的物体.
主要方法:
- 采用以示例为指导的强化学习来培养技能.
- 使用具有可变形,压力感应皮肤的人形机器人 (Punyo机器人).
- 进行模拟训练与域随机化进行模拟到真实转移.
主要成果:
- 成功生成了对日常物品 (水,盒子) 全身操纵的策略.
- 经过盲目的灵巧操作, 仅使用自感和触觉反.
- 通过机器人合规和域随机化实现了强大的政策转移到硬件中.
结论:
- 以示例为指导的强化学习对于教导机器人整体操纵是有效的.
- 机器人遵守是成功操纵全身的人形机器人的关键.
- 开发的方法使机器人能够以最少的感知能力执行复杂的操纵任务.
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