BlenDAgger: Blended Shared Control for Interactive Imitation Learning
Robot policies are frequently trained from human corrections, yet teleoperating a robot to provide corrections is burdensome, and human demonstrators are not always optimal. We propose Blended DAgger (BlenDAgger), an approach for collecting data to train imitation learning policies by using shared control to blend the policy's and demonstrator's actions during interventions. By blending human and policy actions, we aim to improve the autonomous performance of manipulation policies. We validate our approach across five manipulation tasks, two in the real world and three in simulation. Our approach achieves higher autonomous performance by 30 or more percentage points on two real-world task...
| Authors | Cailyn Smith, Geoffrey Sun, Henny Admoni et al. |
|---|---|
| Primary category | cs.RO |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
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