DANLI: Deliberative Agent for Following Natural Language Instructions
This addresses the limitation of reactive agents in embodied AI for instruction following, offering improved performance and transparency for complex tasks.
The paper tackles the problem of reactive embodied AI agents being insufficient for long-horizon complex tasks by proposing a neuro-symbolic deliberative agent that applies reasoning and planning, achieving over 70% improvement over reactive baselines on the TEACh benchmark.
Recent years have seen an increasing amount of work on embodied AI agents that can perform tasks by following human language instructions. However, most of these agents are reactive, meaning that they simply learn and imitate behaviors encountered in the training data. These reactive agents are insufficient for long-horizon complex tasks. To address this limitation, we propose a neuro-symbolic deliberative agent that, while following language instructions, proactively applies reasoning and planning based on its neural and symbolic representations acquired from past experience (e.g., natural language and egocentric vision). We show that our deliberative agent achieves greater than 70% improvement over reactive baselines on the challenging TEACh benchmark. Moreover, the underlying reasoning and planning processes, together with our modular framework, offer impressive transparency and explainability to the behaviors of the agent. This enables an in-depth understanding of the agent's capabilities, which shed light on challenges and opportunities for future embodied agents for instruction following. The code is available at https://github.com/sled-group/DANLI.