A Hierarchical Approach to Imitation Learning for Manipulation Tasks Requiring Time Varying Forces

Abstract: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 recent visual-tactile approaches that focus on positional correction, DPA-FTG decouples low-frequency planning from high-frequency force regulation. At the high level ($5$ Hz), a conditional diffusion model predicts a sequence of latent parameters for selecting a strategy from a learned vocabulary of task primitives. At the low level ($60$ Hz), a lightweight, force-conditioned policy acts as a neural impedance controller, modulating execution in real-time to maintain contact stability. We validate our approach on a bimanual battery disassembly task involving the separation of a compliant sheet. Experimental evaluation demonstrates that DPA-FTG outperforms state-of-the-art baselines, including Reactive Diffusion Policy (RDP).

Robotics (cs.RO); Artificial Intelligence (cs.AI)

arXiv:2608.03103 [cs.RO]

(or arXiv:2608.03103v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2608.03103 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.1016/j.rcim.2026.103309 Focus to learn more DOI(s) linking to related resources

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