Summary of Lota-bench: Benchmarking Language-oriented Task Planners For Embodied Agents, by Jae-woo Choi and Youngwoo Yoon and Hyobin Ong and Jaehong Kim and Minsu Jang
LoTa-Bench: Benchmarking Language-oriented Task Planners for Embodied Agents
by Jae-Woo Choi, Youngwoo Yoon, Hyobin Ong, Jaehong Kim, Minsu Jang
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 This paper proposes a benchmark system to evaluate the performance of task planning for home-service embodied agents using large language models (LLMs). The authors demonstrate the effectiveness of their approach by testing various LLMs and prompts on two pairs of datasets and simulators. The study explores enhancements to the baseline planner and provides valuable insights into the impact of pre-trained model selection and prompt construction on task planning performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special tool to help people make better plans for robots that do chores in homes. They want to see which language models are best at making these plans, so they tested different ones using two types of data and simulations. By doing this, they hope to make it easier for others to develop better planning tools. |
Keywords
* Artificial intelligence * Prompt
