HM3D-M3: A Benchmark for Multi-Agent, Multi-Modal, Multi-Object Goal Navigation
Rapid progress in AI agents and robotics makes it a matter of the near future that humans are served by a team of robots.
ProofPaper ↗
Key points
- Existing benchmarks study multi-agent, multi-modal, and multi-object goal navigation in isolation, and a benchmark that places all three in one episode is missing.
- To fill this gap, we introduce HM3D-M3.
- A team of K agents starts at distributed locations in a photorealistic indoor scene and searches for an unordered set of M object goals, each given as an open-vocabulary category, an instance image, or an instance description.
- HiGA is a trained hierarchical goal assigner on top of a frozen open-vocabulary navigation policy, and LaViRA-M3 is a training-free team of VLM agents with shared mapping and progress analysis.
Sources (1)
- [1]HM3D-M3: A Benchmark for Multi-Agent, Multi-Modal, Multi-Object Goal NavigationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 12:44 PM
Rapid progress in AI agents and robotics makes it a matter of the near future that humans are served by a team of robots.
Existing benchmarks study multi-agent, multi-modal, and multi-object goal navigation in isolation, and a benchmark that places all three in one episode is missing.
Extractive summary: sentences quoted from the sources.
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