Robotics
Spatially-Enhanced Recurrent Memory for Long-Range Mapless Navigation
•January 5, 2026
Original Description
Can recurrent neural networks really understand space? We discovered that standard RNNs like LSTMs and GRUs excel at capturing time, but struggle with space.
We're introducing Spatially-Enhanced Recurrent Units (SRUs) — a simple yet powerful modification that enables robots to build implicit spatial memories for navigation. Published in the International Journal of Robotics Research (IJRR), this work demonstrates up to +105% improvement over baseline approaches, with robots successfully navigating 70+ meters in the real world using only a single forward-facing camera.
Key Results:
• +105% vs. stacked frames (GTRL baseline)
• +29.6% vs. explicit mapping (EMHP baseline)
• +23.5% vs. standard RNNs (LSTM/GRU)
Real-World Deployment:
✅ Zero-shot transfer from simulation
✅ 70m+ goal distances in forests
✅ 100m+ traversal in single missions
✅ Indoor offices, terraces, and complex natural terrain
Learn more and explore the code at: https://michaelfyang.github.io/sru-project-website
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