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Found 5 papers, 2 papers with code
Online Continual Learning For Interactive Instruction Following Agents
To take a step towards a more realistic embodied agent learning scenario, we propose two continual learning setups for embodied agents; learning new behaviors (Behavior Incremental Learning, Behavior-IL) and new environments (Environment Incremental Learning, Environment-IL) For the tasks, previous 'data prior' based continual learning methods maintain logits for the past tasks.
Multi-Level Compositional Reasoning for Interactive Instruction Following
At the middle level, we discriminatively control the agent's navigation by a master policy by alternating between a navigation policy and various independent interaction policies.
Context-Aware Planning and Environment-Aware Memory for Instruction Following Embodied Agents
Accomplishing household tasks requires to plan step-by-step actions considering the consequences of previous actions.
Hierarchical Modular Framework for Long Horizon Instruction Following
To address such composite tasks, we propose a hierarchical modular approach to learn agents that navigate and manipulate objects in a divide-and-conquer manner for the diverse nature of the entailing tasks.
Factorizing Perception and Policy for Interactive Instruction Following
Performing simple household tasks based on language directives is very natural to humans, yet it remains an open challenge for AI agents.
