Context-Aware Planning and Environment-Aware Memory for Instruction Following Embodied Agents
This addresses the challenge of improving visual navigation and object interaction for embodied agents in household tasks, representing a strong specific gain.
The paper tackled the problem of embodied agents making mistakes in household tasks by proposing CAPEAM, which incorporates semantic context and environment changes into planning, achieving state-of-the-art performance with up to +10.70% improvement in unseen environments.
Accomplishing household tasks requires to plan step-by-step actions considering the consequences of previous actions. However, the state-of-the-art embodied agents often make mistakes in navigating the environment and interacting with proper objects due to imperfect learning by imitating experts or algorithmic planners without such knowledge. To improve both visual navigation and object interaction, we propose to consider the consequence of taken actions by CAPEAM (Context-Aware Planning and Environment-Aware Memory) that incorporates semantic context (e.g., appropriate objects to interact with) in a sequence of actions, and the changed spatial arrangement and states of interacted objects (e.g., location that the object has been moved to) in inferring the subsequent actions. We empirically show that the agent with the proposed CAPEAM achieves state-of-the-art performance in various metrics using a challenging interactive instruction following benchmark in both seen and unseen environments by large margins (up to +10.70% in unseen env.).