Neuro-Inspired Efficient Map Building via Fragmentation and Recall

by   Jaedong Hwang, et al.

Animals and robots navigate through environments by building and refining maps of the space. These maps enable functions including navigating back to home, planning, search, and foraging. In large environments, exploration of the space is a hard problem: agents can become stuck in local regions. Here, we use insights from neuroscience to propose and apply the concept of Fragmentation-and-Recall (FarMap), with agents solving the mapping problem by building local maps via a surprisal-based clustering of space, which they use to set subgoals for spatial exploration. Agents build and use a local map to predict their observations; high surprisal leads to a “fragmentation event” that truncates the local map. At these events, the recent local map is placed into long-term memory (LTM), and a different local map is initialized. If observations at a fracture point match observations in one of the stored local maps, that map is recalled (and thus reused) from LTM. The fragmentation points induce a natural online clustering of the larger space, forming a set of intrinsic potential subgoals that are stored in LTM as a topological graph. Agents choose their next subgoal from the set of near and far potential subgoals from within the current local map or LTM, respectively. Thus, local maps guide exploration locally, while LTM promotes global exploration. We evaluate FarMap on complex procedurally-generated spatial environments to demonstrate that this mapping strategy much more rapidly covers the environment (number of agent steps and wall clock time) and is more efficient in active memory usage, without loss of performance.


page 4

page 7

page 13

page 17


Egocentric Spatial Memory

Egocentric spatial memory (ESM) defines a memory system with encoding, s...

Self-Exploration in Complex Unknown Environments using Hybrid Map Representation

A hybrid map representation, which consists of a modified generalized Vo...

Interpreting Distortions in Dimensionality Reduction by Superimposing Neighbourhood Graphs

To perform visual data exploration, many dimensionality reduction method...

Maplets: An Efficient Approach for Cooperative SLAM Map Building Under Communication and Computation Constraints

This article introduces an approach to facilitate cooperative exploratio...

Open-ended search for environments and adapted agents using MAP-Elites

Creatures in the real world constantly encounter new and diverse challen...

Active Robotic Mapping through Deep Reinforcement Learning

We propose an approach to learning agents for active robotic mapping, wh...

Efficient Global Occupancy Mapping for Mobile Robots using OpenVDB

In this work we present a fast occupancy map building approach based on ...

Please sign up or login with your details

Forgot password? Click here to reset