# RAM: Reachability Across Morphologies

arXiv: https://arxiv.org/abs/2606.09108 · HTML: https://mlsanity.com/paper/2606.09108/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 67 references that are in the map.
- First posted on arXiv 8 Jun 2026.

## Builds on (most cited first, 22 of 22)

- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
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- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [Understanding Deep Image Representations by Inverting Them](https://mlsanity.com/paper/1412.0035/) (2014)
- [WIRE: Wavelet Implicit Neural Representations](https://mlsanity.com/paper/2301.05187/) (2023)
- [Inverting Visual Representations with Convolutional Networks](https://mlsanity.com/paper/1506.02753/) (2015)
- [Optimistix: modular optimisation in JAX and Equinox](https://mlsanity.com/paper/2402.09983/) (2024)
- [IKFlow: Generating Diverse Inverse Kinematics Solutions](https://mlsanity.com/paper/2111.08933/) (2021)
- [Representing Robot Geometry as Distance Fields: Applications to Whole-body Manipulation](https://mlsanity.com/paper/2307.00533/) (2023)
- [Configuration Space Distance Fields for Manipulation Planning](https://mlsanity.com/paper/2406.01137/) (2024)
- [Generative Graphical Inverse Kinematics](https://mlsanity.com/paper/2209.08812/) (2022)
- [Comparison of Distal Teacher Learning with Numerical and Analytical Methods to Solve Inverse Kinematics for Rigid-Body Mechanisms](https://mlsanity.com/paper/2003.00225/) (2020)
- [Automatic Geometric Decomposition for Analytical Inverse Kinematics](https://mlsanity.com/paper/2409.14815/) (2024)
- [A Design Co-Pilot for Task-Tailored Manipulators](https://mlsanity.com/paper/2509.13077/) (2025)
- [scipy.spatial.transform: Differentiable Framework-Agnostic 3D Transformations in Python](https://mlsanity.com/paper/2511.18157/) (2025)
- [Learning Differentiable Reachability Maps for Optimization-based Humanoid Motion Generation](https://mlsanity.com/paper/2508.11275/) (2025)
- [HJCD-IK: GPU-Accelerated Inverse Kinematics through Batched Hybrid Jacobian Coordinate Descent](https://mlsanity.com/paper/2510.07514/) (2025)

## Abstract (arXiv)

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. RAM is trained on a large-scale dataset of 3·1010 samples generated solely from forward kinematics, which we publish alongside the model. Experiments show that RAM reaches 84% balanced accuracy at nanosecond inference, 15 percentage points higher than a generative inverse kinematics baseline that also generalises across morphologies, while running three orders of magnitude faster. This speed carries over to downstream tasks, accelerating morphology and trajectory optimisation by one to two orders of magnitude.