Ask4Help: Learning to Leverage an Expert for Embodied Tasks
This addresses the gap in deploying AI agents in real-world applications by reducing expert query costs, though it is incremental as it builds on existing agent frameworks.
The paper tackles the problem of improving the performance and reliability of embodied AI agents by enabling them to request expert assistance, resulting in substantial performance gains: for object navigation, success increased from 52% to 86% with 13% help, and for room rearrangement, from 7% to 90.4% with 39% help.
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 Ask4Help policy that augments agents with the ability to request, and then use expert assistance. Ask4Help 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 Ask4Help 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 Ask4Help demonstrate the efficacy of our approach in practical scenarios. We release the code for Ask4Help here: https://github.com/allenai/ask4help.