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arXiv · 2509.20297

mindmap: Spatial Memory in Deep Feature Maps for 3D Action Policies

Remo Steiner·Alexander Millane·David Tingdahl·Clemens Volk·Vikram Ramasamy·Xinjie Yao·Peter Du·Soha Pouya·Shiwei Sheng
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Abstract

End-to-end learning of robot control policies, structured as neural networks, has emerged as a promising approach to robotic manipulation. To complete many common tasks, relevant objects are required to pass in and out of a robot's field of view. In these settings, spatial memory - the ability to remember the spatial composition of the scene - is an important competency. However, building such mechanisms into robot learning systems remains an open research problem. We introduce mindmap (Spatial Memory in Deep Feature Maps for 3D Action Policies), a 3D diffusion policy that generates robot trajectories based on a semantic 3D reconstruction of the environment. We show in simulation experiments that our approach is effective at solving tasks where state-of-the-art approaches without memory mechanisms struggle. We release our reconstruction system, training code, and evaluation tasks to spur research in this direction.

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Remo Steiner, Alexander Millane, David Tingdahl, Clemens Volk, Vikram Ramasamy, Xinjie Yao, Peter Du, Soha Pouya, Shiwei Sheng. 2025-09-24. mindmap: Spatial Memory in Deep Feature Maps for 3D Action Policies. https://arxiv.org/abs/2509.20297

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