arXiv:2606.09108v1 Announce Type: cross 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\c
The proliferation of various robotic morphologies necessitates a more generalized and efficient method for workspace analysis, driving demand for AI-driven solutions.
This development significantly accelerates the design, simulation, and operation stages for a diverse range of robotic platforms, enhancing efficiency and reducing development costs.
Robot designers and operators can now rapidly assess reachable workspaces for novel or existing robotic systems, including those with unseen morphologies, with greater accuracy and less computational overhead.
- · Robotics R&D
- · Automation industry
- · Robot manufacturers
- · AI/ML companies
- · Traditional simulation software vendors
- · Manual robotic design processes
Faster and cheaper development of new robotic forms and applications becomes possible.
This could lead to a broader adoption of specialized robots in various industries, including those with complex and constrained environments.
The acceleration of robotic design cycles might trigger a virtuous cycle of innovation, making advanced robotics more accessible and customizable for a wider array of tasks.
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Read at arXiv cs.LG