Training and Evaluating Diffusion Policies with Long Context Lengths
Imitation learning has enabled highly-dexterous robotic manipulation from RGB observations. Policies trained with these methods, however, typically condition robot actions on only a short history of observations. These policies cannot solve tasks that require memory and can get stuck repeatedly executing the same failing motions. In this work, we first benchmark policy performance as context length is incrementally increased from short to long, across a spectrum of tasks with varying local stability and memory requirements, and in multiple data regimes. To our knowledge, this is the first study to investigate context length for Diffusion Policies at this level of detail. Our results chall...
| Authors | Abhinav Agarwal, Adam Wei, Taylan Kargin et al. |
|---|---|
| Primary category | cs.RO |
| Published | 15 June 2026 |
| Last updated at source | 9 July 2026 |
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