机器人学动作，别再只给一种标准答案
教机器人模仿人类动作，最怕训练数据太单一：如果只反复演示一种成功方式，机器人一遇到新情况就懵。这篇的思路是反过来用强化学习当「数据工厂」，专门生成多种不同但都能成功的操作轨迹——比如抓杯子，既可以从上往下抓，也可以从侧面推倒再抓。实验里，用这批多样数据训练出的机器人，在仿真和真机上的成功率分别从40%提到62%、从39%提到78%。它不是你明天就能装进自家机器人的东西，但指向一个更本质的转变：与其教机器人「标准答案」，不如让它见识「正确答案有很多种」。
📄 原文摘要(英文)
Behavior cloning for robotic manipulation is highly sensitive to the coverage of its training data, yet collecting diverse successful demonstrations remains costly. We study reinforcement learning (RL) not as a deployable controller, but as a data generator for downstream visuomotor imitation learning. Our key premise is that multiple successful solution modes cover a broader range of successful trajectories than repeatedly collecting a single narrow solution. We introduce DiscoDemo, which trains a privileged latent-conditioned RL generator with a parallel reverse curriculum and a success-gated diversity objective to discover diverse solutions while preserving task success. The trained generator produces multiple successful closed-loop behaviors, which are safety-filtered and exported as a static dataset for downstream imitation learning. Across four manipulation tasks, DiscoDemo produces broader successful-state coverage and improves downstream visuomotor policies in both simulation and the real world. Averaged across tasks, downstream success increases to 62% from 40% for the strongest baseline in simulation, and to 78% from 39% on the real robot, respectively. These results suggest that successful solution diversity improves imitation learning for a pretrained flow-matching VLA and remains complementary to corrective data collection. Project page: https://davian-robotics.github.io/DiscoDemo