Remember Where You Walk: Learning Spatial Memory for End-to-End Legged Robot Exploration

Key points

The authors proposed LSMM, a position-based matrix memory that lets reinforcement learning agents store task information at specific memory cells.
LSMM updates only a 5 × 5 cell window centered on the robot's position estimate at every step, leaving the rest of the memory unchanged so information about past locations persists.
The policy was trained end-to-end with PPO together with a maze reconstruction loss, where an auxiliary head predicts the maze wall layout from memory features.
On 100 unseen evaluation mazes, all architectures reached a comparable coverage of 65.4 to 82.8 %, so pure exploration performance alone did not require a spatial memory.
On maze reconstruction error, LSMM was the only architecture that significantly reduced it while exploring, from 0.33 to 0.1, more than three times lower than any baseline.
As limitations, the memory covers a fixed area sized to the maze, and the update depends on an external position estimate, since the position is not yet estimated from onboard sensing alone.

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