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.

翻译：机器人生命周期的多个阶段（从形态生成到操作执行）本质上依赖于可达工作空间。然而，当前用于近似工作空间的方法存在速度慢、精度低或仅适用于单一形态的局限。我们提出跨越形态的可达性分析（RAM）：一种以形态为条件的隐式神经表示方法，可作为姿态可达性快速可微的代理模型，在固有考虑自碰撞的同时泛化至未见形态。为训练RAM，我们发布了一个基于前向运动学生成的$3\cdot10^{10}$样本量的大规模数据集。实验表明，我们的模型在纳秒级推理中达到$86\%$的$F_1$分数，比基线高出$14\%$，同时将推理时间降低了三个数量级。我们进一步证明，该方法在基于梯度的形态优化和轨迹优化中分别实现了一个和两个数量级的加速。网站：https://timwalter.github.io/ram。