Diffusion Meets DAgger: Supercharging Eye-in-hand Imitation Learning
This addresses the high cost of data collection for robust imitation learning in robotics, particularly for eye-in-hand tasks, though it is incremental as it builds on existing DAgger and diffusion methods.
The paper tackles the problem of compounding execution errors in imitation learning by proposing Diffusion Meets DAgger (DMD), which uses diffusion models to synthesize out-of-distribution samples instead of collecting expensive new data, achieving success rates of 80% in pushing with 8 demonstrations, 92% in stacking, 80% in pouring, and 90% in shirt hanging.
A common failure mode for policies trained with imitation is compounding execution errors at test time. When the learned policy encounters states that are not present in the expert demonstrations, the policy fails, leading to degenerate behavior. The Dataset Aggregation, or DAgger approach to this problem simply collects more data to cover these failure states. However, in practice, this is often prohibitively expensive. In this work, we propose Diffusion Meets DAgger (DMD), a method to reap the benefits of DAgger without the cost for eye-in-hand imitation learning problems. Instead of collecting new samples to cover out-of-distribution states, DMD uses recent advances in diffusion models to synthesize these samples. This leads to robust performance from few demonstrations. We compare DMD against behavior cloning baseline across four tasks: pushing, stacking, pouring, and shirt hanging. In pushing, DMD achieves 80% success rate with as few as 8 expert demonstrations, where naive behavior cloning reaches only 20%. In stacking, DMD succeeds on average 92% of the time across 5 cups, versus 40% for BC. When pouring coffee beans, DMD transfers to another cup successfully 80% of the time. Finally, DMD attains 90% success rate for hanging shirt on a clothing rack.