Summary of Goat-bench: a Benchmark For Multi-modal Lifelong Navigation, by Mukul Khanna et al.
GOAT-Bench: A Benchmark for Multi-Modal Lifelong Navigation
by Mukul Khanna, Ram Ramrakhya, Gunjan Chhablani, Sriram Yenamandra, Theophile Gervet, Matthew Chang, Zsolt Kira, Devendra Singh Chaplot, Dhruv Batra, Roozbeh Mottaghi
First submitted to arxiv on: 9 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper introduces a universal navigation model capable of handling various goal types, enabling more effective user interaction with robots. The GOAT-Bench benchmark is designed for this task, where an agent navigates to a sequence of targets specified by category name, language description, or image in an open-vocabulary fashion. The paper benchmarks monolithic RL and modular methods on the GOAT task, analyzing their performance across modalities, the role of explicit and implicit scene memories, robustness to noise in goal specifications, and the impact of memory in lifelong scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new benchmark for navigation models that can handle different types of targets. It’s like giving robots a map with multiple destinations, and they have to figure out how to get to each one. The researchers tested two types of models on this task and found out how well they do in different situations. |
