Summary of Benchmark Real-time Adaptation and Communication Capabilities Of Embodied Agent in Collaborative Scenarios, by Shipeng Liu et al.
Benchmark Real-time Adaptation and Communication Capabilities of Embodied Agent in Collaborative Scenarios
by Shipeng Liu, Boshen Zhang, Zhehui Huang
First submitted to arxiv on: 30 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC); 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 Advancements in Large Language Models (LLMs) have revolutionized human-robot interaction, particularly in collaborative settings. However, real-time collaboration requires agents to adapt dynamically to unseen human behaviors while maintaining effective communication. Existing benchmarks fall short in evaluating embodied agent adaptability, focusing on task performance rather than reactive adaptation. To address this gap, we propose a novel benchmark assessing reactively adaptable and communicative embodied agents at every step. Our Monitor-then-Adapt (MonTA) framework combines strong adaptability and real-time execution. MonTA consists of three LLM modules: a lightweight Monitor for high-frequency monitoring and two proficient Adapters for low-frequency subtask and path adaptation reasoning. Our results demonstrate that MonTA outperforms baseline agents on our proposed benchmark, with further user studies confirming the high reasonability of our framework’s adaptation plan and language instruction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about improving how humans and robots work together in real-time. Right now, robots can’t adapt to new human actions quickly enough or communicate well. The authors created a new way to test robot-human collaboration that focuses on the robot’s ability to react to changing situations. They also developed a special framework called Monitor-then-Adapt (MonTA) that helps robots work better with humans in real-time. MonTA has three parts: one for quickly monitoring changes and two others for adapting to new situations. The authors tested their ideas and found that MonTA works better than other approaches, which is important because it means humans and robots can work together more effectively. |
