用少量演示+自动课程学习，让机械手在真实世界完成复杂操作
DemoStart: Demonstration-led auto-curriculum applied to sim-to-real with multi-fingered robots
- 基于演示引导自动设计训练课程，减少对大量数据依赖
- 仅需100次仿真演示即实现真实机器人零样本迁移，效果优于直接模仿
- 直接从多视角图像和传感器数据训练，适合真实场景部署
我们提出DemoStart，一种新型自主课程强化学习方法，可在仅具备稀疏奖励和少量示范的情况下，让三指机械手在仿真环境中学习复杂操作行为。通过仿真训练显著缩短行为开发周期，并利用领域随机化技术实现零样本的模拟到现实迁移。迁移后的策略直接从多摄像头原始像素和机器人本体感知数据中学习。该方法在真实机器人上的表现优于仅依赖示范学习的策略，且所需演示次数仅为后者的1/100，全部演示均在仿真中完成。更多细节与视频见https://sites.google.com/view/demostart。
原文摘要 · Abstract (English)
We present DemoStart, a novel auto-curriculum reinforcement learning method capable of learning complex manipulation behaviors on an arm equipped with a three-fingered robotic hand, from only a sparse reward and a handful of demonstrations in simulation. Learning from simulation drastically reduces the development cycle of behavior generation, and domain randomization techniques are leveraged to achieve successful zero-shot sim-to-real transfer. Transferred policies are learned directly from raw pixels from multiple cameras and robot proprioception. Our approach outperforms policies learned from demonstrations on the real robot and requires 100 times fewer demonstrations, collected in simulation. More details and videos in https://sites.google.com/view/demostart.
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