Optimizing Neurorobot Policy under Limited Demonstration Data through Preference Regret
Robot reinforcement learning from demonstrations (RLfD) assumes that expert data is abundant; this is usually unrealistic in the real world given data scarcity as well as high collection cost. Furthermore, imitation learning algorithms assume that the data is independently and identically distributed, which ultimately results in poorer performance as gradual errors emerge and compound within test-time trajectories. We address these issues by introducing the "master your own expertise" (MYOE) framework, a self-imitation framework that enables robotic agents to learn complex behaviors from limited demonstration data samples. Inspired by human perception and action, we propose and design wha...
| Authors | Viet Dung Nguyen, Yuhang Song, Anh Nguyen et al. |
|---|---|
| Primary category | cs.RO |
| Published | 4 April 2026 |
| Last updated at source | 4 April 2026 |
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