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Remember Where You Walk: Learning Spatial Memory for End-to-End Legged Robot Exploration
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First public release: 2026-10-27 · Indexed by Pub-AI: 2026-10-28 UTC
Remember Where You Walk: Learning Spatial Memory for End-to-End Legged Robot Exploration
Preprint posted: 2026-10-27 · Original record ↗
- arxiv
- 2610.37203
Explicit source versions (1)
- v1 · 2026-10-27
- Preprint available
Source date: 2026-10-27 · First detected: 2026-10-28 UTC · Historical baseline
Source evidence (2)
- Official evidence ↗ · RSS guid=oai:arXiv.org:2610.37203v1; arxiv:announce_type=new; item pubDate (official metadata)
- Official evidence ↗ · atom:published (official metadata)
Abstract · arxiv
Humanoids and other legged robots operating in complex environments must remember information that leaves their field of view and only becomes relevant much later. Reinforcement learning policies address this using memory networks such as LSTMs that are trained end-to-end with the policy to memorize such task-relevant aspects. Yet, such architectures often fail to preserve sufficient information over long horizons. In this work, we propose a network architecture based on Hadamard memory that specifically targets robot navigation. Our Local Spatial Matrix Memory (LSMM) enables RL agents to store generic task-relevant information at specific, position-based memory cells, enabling flexible and spatially grounded memorization. We evaluate LSMM on the task of autonomous maze exploration, where it matches the maze coverage of strong recurrent and finite-history baselines while being the only architecture that remembers the maze layout, reducing the reconstruction error threefold.