RAM: Reachability Across Morphologies

Abstract:Many stages of the robotic lifecycle, from morphology synthesis to operation, rely fundamentally on the reachable workspace. However, current methods for approximating workspaces are slow, imprecise, or tied to a single morphology. We introduce Reachability Across Morphologies (RAM): a morphology-conditioned, implicit neural representation that acts as a fast, differentiable surrogate for pose reachability, generalising to unseen morphologies while inherently accounting for self-collisions. To train RAM, we publish a large-scale dataset of $3\cdot10^{10}$ samples generated solely from forward kinematics. Experiments show that our model achieves an $ F_1$-score of $86\%$ at nanosecond inference, outperforming the baseline by $14\%$ while reducing inference time by three orders of magnitude. We further demonstrate speed-ups of one and two orders of magnitude for gradient-based morphology and trajectory optimisation, respectively. Website: this https URL.

Robotics (cs.RO); Machine Learning (cs.LG)

arXiv:2606.09108 [cs.RO]

(or arXiv:2606.09108v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2606.09108 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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