<ul><li>Recent advancements in imitation learning have led to transformer-based behavior foundation models (BFMs) that enable multi-modal, human-like control for humanoid agents.</li><li>The introduction of "Task Tokens" provides a method to tailor BFMs to specific tasks while maintaining flexibility.</li><li>Task Tokens leverage the transformer architecture of BFMs to learn a task-specific encoder through reinforcement learning, allowing the incorporation of user-defined priors and balancing reward design.</li><li>Task Tokens demonstrate efficacy in various tasks, including out-of-distribution scenarios, and are compatible with other prompting modalities.</li></ul>

Task Tokens: A Flexible Approach to Adapting Behavior Foundation Models

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