• DOI:10.48550/arXiv.2211.09960
• Corpus ID: 253708045

Ask4Help: Learning to Leverage an Expert for Embodied Tasks

@article{Singh2022Ask4HelpLT,
  title={Ask4Help: Learning to Leverage an Expert for Embodied Tasks},
  author={Kunal Pratap Singh and Luca Weihs and Alvaro Herrasti and Jonghyun Choi and Aniruddha Kemhavi and Roozbeh Mottaghi},
  journal={ArXiv},
  year={2022},
  volume={abs/2211.09960}
}

• Kunal Pratap Singh, Luca Weihs, +3 authors Roozbeh Mottaghi
• Published 18 November 2022
• Computer Science
• ArXiv

Embodied AI agents continue to become more capable every year with the advent of new models, environments, and benchmarks, but are still far away from being performant and reliable enough to be deployed in real, user-facing, applications. In this paper, we ask: can we bridge this gap by enabling agents to ask for assistance from an expert such as a human being? To this end, we propose the A SK 4H ELP policy that augments agents with the ability to request, and then use expert assistance. A SK 4H ELP policies can be efficiently trained without modifying the original agent’s parameters and learn a desirable trade-off between task performance and the amount of requested help, thereby reducing the cost of querying the expert. We evaluate A SK 4H ELP on two different tasks – object goal navigation and room rearrangement and see substantial improvements in performance using minimal help. On object navigation, an agent that achieves a 52% success rate is raised to 86% with 13% help and for rearrangement, the state-of-the-art model with a 7% success rate is dramatically improved to 90 . 4% using 39% help. Human trials with A SK 4H ELP demonstrate the efficacy of our approach in practical scenarios. We release the code for Ask4Help here: https: //github.com/allenai/ask4help .