Anticipate, Adapt, Act: A Hybrid Framework for Task Planning

Anticipating and adapting to failures is a key capability robots need to collaborate effectively with humans in complex domains. This continues to be a challenge despite the impressive performance of state of the art AI planning systems and Large Language Models (LLMs) because of the uncertainty associated with the tasks and their outcomes. Toward addressing this challenge, we present a hybrid framework that integrates the generic prediction capabilities of an LLM with the probabilistic sequential decision-making capability of Relational Dynamic Influence Diagram Language. For any given task, the robot reasons about the task and the capabilities of the human attempting to complete it; predicts potential failures due to lack of ability (in the human) or lack of relevant domain objects; and executes actions to prevent such failures or recover from them. Experimental evaluation in the VirtualHome 3D simulation environment demonstrates substantial improvement in performance compared with state of the art baselines.

AdaptBot: Combining LLM with Knowledge Graphs and Human Input for Generic-to-Specific Task Decomposition and Knowledge Refinement

Embodied agents assisting humans are often asked to complete a new task in a new scenario. An agent preparing a particular dish in the kitchen based on a known recipe may be asked to prepare a new dish or to perform cleaning tasks in the storeroom. There may not be sufficient resources, e.g., time or labeled examples, to train the agent for these new situations. Large Language Models (LLMs) trained on considerable knowledge across many domains are able to predict a sequence of abstract actions for such new tasks and scenarios, although it may not be possible for the agent to execute this action sequence due to task-, agent-, or domain-specific constraints. Our framework addresses these challenges by leveraging the generic predictions provided by LLM and the prior domain-specific knowledge encoded in a Knowledge Graph (KG), enabling an agent to quickly adapt to new tasks and scenarios. The robot also solicits and uses human input as needed to refine its existing knowledge. Based on experimental evaluation over cooking and cleaning tasks in simulation domains, we demonstrate that the interplay between LLM, KG, and human input leads to substantial performance gains compared with just using the LLM output.