A Hierarchical Approach to Imitation Learning for Manipulation Tasks Requiring Time Varying Forces
Diffusion policies have shown strong performance in learning complex, multi-modal behaviors for robotic manipulation. However, their application to contact-rich disassembly tasks remains limited by a key trade-off: the iterative denoising process introduces inference latencies that makes high frequency control difficult, which is essential for realizing dynamic interactions such as chiseling and prying. Recent action-chunking techniques mitigate latency but use an open-loop execution window, rendering the system blind to rapid force transients caused by fracture events. To bridge this gap, we introduce the Diffusion Policy Augmented by Fast Trajectory Generation (DPA-FTG). Compared to rec...
| Authors | Rishabh Shukla, Adithya Santhosh, Shaili Gandhi et al. |
|---|---|
| Primary category | cs.RO |
| Published | 4 August 2026 |
| Last updated at source | 4 August 2026 |
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