RAM: Reachability Across Morphologies
2026-06-08 • Robotics
RoboticsMachine Learning
AI summaryⓘ
The authors created a new method called Reachability Across Morphologies (RAM) that quickly predicts which positions a robot arm can physically reach, no matter its shape. Unlike older methods, their model is very fast, accurate, and works for many different robot designs while avoiding the robot bumping into itself. They trained it on a huge dataset from basic robot movements and showed it performs better and much faster than previous approaches. This makes designing and controlling robots more efficient.
robotic workspacemorphologyreachabilityimplicit neural representationforward kinematicsself-collisiongradient optimizationpose reachabilityrobot trajectory planningmachine learning model
Authors
Tim Walter, Xinyu Chen, Jonathan Külz, Matthias Althoff
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: https://timwalter.github.io/ram.