RECORD
Active Neural Topological Mapping for Multi-Agent Exploration
Robotics · Submitted 1 Nov 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Extensive experiments conducted in a physically-realistic simulator, Habitat, demonstrate that MANTM reduces the steps by at least 26.40% over planning-based baselines and by at least 7.63% over RL-based competitors in unseen scenarios.
The compiled record
The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
Open the compiled record
The abstract, sentence by sentence
This paper investigates the multi-agent cooperative exploration problem, which requires multiple agents to explore an unseen environment via sensory signals in a limited time. A popular approach to exploration tasks is to combine active mapping with planning. Metric maps capture the details of the spatial representation, but are with high communication traffic and may vary significantly between scenarios, resulting in inferior generalization. Topological maps are a promising alternative as they consist only of nodes and edges with abstract but essential information and are less influenced by the scene structures. However, most existing topology-based exploration tasks utilize classical methods for planning, which are time-consuming and sub-optimal due to their handcrafted design. Deep reinforcement learning (DRL) has shown great potential for learning (near) optimal policies through fast end-to-end inference. In this paper, we propose Multi-Agent Neural Topological Mapping (MANTM) to improve exploration efficiency and generalization for multi-agent exploration tasks. MANTM mainly comprises a Topological Mapper and a novel RL-based Hierarchical Topological Planner (HTP). The Topological Mapper employs a visual encoder and distance-based heuristics to construct a graph containing main nodes and their corresponding ghost nodes. The HTP leverages graph neural networks to capture correlations between agents and graph nodes in a coarse-to-fine manner for effective global goal selection. Extensive experiments conducted in a physically-realistic simulator, Habitat, demonstrate that MANTM reduces the steps by at least 26.40% over planning-based baselines and by at least 7.63% over RL-based competitors in unseen scenarios.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | This paper investigates the multi-agent cooperative exploration problem, which requires multiple agents to explore an unseen environment via sensory signals in a limited time. | background 1.00 | 0.14 | 0.02 | asserted 0.98 |
| s1 | A popular approach to exploration tasks is to combine active mapping with planning. | background 1.00 | 0.04 | 0.02 | asserted 0.99 |
| s2 | Metric maps capture the details of the spatial representation, but are with high communication traffic and may vary significantly between scenarios, resulting in inferior generalization. | background 0.88 | 0.09 | 0.02 | hedged possibility 0.59 |
| s3 | Topological maps are a promising alternative as they consist only of nodes and edges with abstract but essential information and are less influenced by the scene structures. | background 1.00 | 0.06 | 0.03 | asserted 0.76 |
| s4 | However, most existing topology-based exploration tasks utilize classical methods for planning, which are time-consuming and sub-optimal due to their handcrafted design. | gap 0.90 | 0.04 | 0.02 | asserted 1.00 |
| s5 | Deep reinforcement learning (DRL) has shown great potential for learning (near) optimal policies through fast end-to-end inference. | background 1.00 | 0.04 | 0.02 | asserted 0.96 |
| s6 | In this paper, we propose Multi-Agent Neural Topological Mapping (MANTM) to improve exploration efficiency and generalization for multi-agent exploration tasks. | contribution 1.00 | 0.86 | 0.19 | asserted 0.94 |
| s7 | MANTM mainly comprises a Topological Mapper and a novel RL-based Hierarchical Topological Planner (HTP). | method 0.79 | 0.86 | 0.05 | asserted 1.00 |
| s8 | The Topological Mapper employs a visual encoder and distance-based heuristics to construct a graph containing main nodes and their corresponding ghost nodes. | method 0.99 | 0.85 | 0.02 | asserted 1.00 |
| s9 | The HTP leverages graph neural networks to capture correlations between agents and graph nodes in a coarse-to-fine manner for effective global goal selection. | method 0.99 | 0.81 | 0.04 | asserted 1.00 |
| s10 | Extensive experiments conducted in a physically-realistic simulator, Habitat, demonstrate that MANTM reduces the steps by at least 26.40% over planning-based baselines and by at least 7.63% over RL-based competitors in unseen scenarios. | result 1.00 | 0.97 | 0.82 | asserted 1.00 |
What was examined, and what was not
- arXiv listing abstract v1
- full text
- figures and tables
- references and citing sentences
- relations to other papers
- P abstract
- S sections
- T body sentences
- C citations
- R cross-paper relations
The paper
Original abstract
This paper investigates the multi-agent cooperative exploration problem, which requires multiple agents to explore an unseen environment via sensory signals in a limited time. A popular approach to exploration tasks is to combine active mapping with planning. Metric maps capture the details of the spatial representation, but are with high communication traffic and may vary significantly between scenarios, resulting in inferior generalization. Topological maps are a promising alternative as they consist only of nodes and edges with abstract but essential information and are less influenced by the scene structures. However, most existing topology-based exploration tasks utilize classical methods for planning, which are time-consuming and sub-optimal due to their handcrafted design. Deep reinforcement learning (DRL) has shown great potential for learning (near) optimal policies through fast end-to-end inference. In this paper, we propose Multi-Agent Neural Topological Mapping (MANTM) to improve exploration efficiency and generalization for multi-agent exploration tasks. MANTM mainly comprises a Topological Mapper and a novel RL-based Hierarchical Topological Planner (HTP). The Topological Mapper employs a visual encoder and distance-based heuristics to construct a graph containing main nodes and their corresponding ghost nodes. The HTP leverages graph neural networks to capture correlations between agents and graph nodes in a coarse-to-fine manner for effective global goal selection. Extensive experiments conducted in a physically-realistic simulator, Habitat, demonstrate that MANTM reduces the steps by at least 26.40% over planning-based baselines and by at least 7.63% over RL-based competitors in unseen scenarios.
Human review
How the literature uses it
What this paper does with the arXiv papers it cites
What it cites · 5 sentences without a confident label
Uses the method or tool of 2 papers
-
We remark that the Local Planner and the Local Policy do not involve multi-agent interactions, so they are directly adopted from ANS [ref] .=-1
Learning to Explore using Active Neural SLAM · Methodology · 1.00
-
We train the by using multi-agent proximal policy optimization (MAPPO) [ref] , which is a multi-agent variant of proximal policy optimization (PPO) [ref] .
Proximal Policy Optimization Algorithms · Reinforcement Learning Design · 0.99
Cites as background 1 paper
-
Metric maps are grid maps where each grid predicts its traversability [ref] .
Learning to Explore using Active Neural SLAM · Spatial Representation · 0.91
Discussion
Know something about this paper? Leave a comment, or highlight any passage above to comment on it.