Research map / Multi-agent systems and games / Human-AI and goal-conditioned
Cognitive Mapping and Planning for Visual Navigation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use joint cognitive mapping and planning instead of an LSTM memory policy | Visual navigation in novel buildings · Geometric navigation (RGB) | success rate: 53.0 -> 62.5 | Helped |
| Use joint cognitive mapping and planning instead of an LSTM memory policy | Visual navigation in novel buildings · Geometric navigation (Depth) | success rate: 71.8 -> 78.3 | Helped |
| Use joint cognitive mapping and planning instead of an LSTM memory policy | Visual navigation in novel buildings · Semantic navigation, aggregate (RGB) | success rate: 23.5 -> 34.2 | Helped |
| Use joint cognitive mapping and planning instead of an LSTM memory policy | Visual navigation in novel buildings · Semantic navigation, aggregate (Depth) | success rate: 27.2 -> 40.0 | Helped |
| Use a learned mapper instead of an analytic mapper (ablation) | CMP · Geometric navigation | navigation performance | Helped |
| Add planning to the navigation architecture (ablation) | CMP · Geometric navigation | navigation performance | Helped |
| Use single-scale planning instead of multi-scale planning (ablation) | CMP · Geometric navigation | navigation performance | Helped |
Closest papers by meaning
- Evolving Graphical Planner: Contextual Global Planning for Vision-and-Language Navigation (NeurIPS 2020 · similarity 0.89)
- Reasoning in Visual Navigation of End-to-end Trained Agents: A Dynamical Systems Approach (CVPR 2025 · similarity 0.88)
- Learning Cognitive Maps from Transformer Representations for Efficient Planning in Partially Observed Environments (ICML 2024 · similarity 0.88)
- Visual Planning: Let's Think Only with Images (ICLR 2026 · similarity 0.88)
- World-aware Planning Narratives Enhance Large Vision-Language Model Planner (NeurIPS 2025 · similarity 0.87)
- NavGPT: Explicit Reasoning in Vision-and-Language Navigation with Large Language Models (AAAI 2024 · similarity 0.87)
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