Bridging Values and Behavior: A Hierarchical Framework for Proactive Embodied Agents
This work addresses the problem of enabling embodied agents to act autonomously based on abstract values rather than passive instructions, which is important for developing truly autonomous AI systems.
ValuePlanner introduces a hierarchical cognitive architecture that decouples high-level value scheduling from low-level action execution, enabling embodied agents to generate coherent, long-horizon, self-directed behavior in a household environment, outperforming instruction-following and needs-driven baselines.
Current embodied agents are often limited to passive instruction-following or reactive need-satisfaction, lacking a stable, high-order value framework essential for long-term, self-directed behavior and resolving motivational conflicts. We introduce \textit{ValuePlanner}, a hierarchical cognitive architecture that decouples high-level value scheduling from low-level action execution. \textit{ValuePlanner} employs an LLM-based cognitive module to generate symbolic subgoals by reasoning through abstract value trade-offs, which are then translated into executable action plans by a classical PDDL planner. This process is refined via a closed-loop feedback mechanism. Evaluating such autonomy requires methods beyond task-success rates, and we therefore propose a value-centric evaluation suite measuring cumulative value gain, preference alignment, and behavioral diversity. Experiments in the TongSim household environment demonstrate that \textit{ValuePlanner} arbitrates competing values to generate coherent, long-horizon, self-directed behavior absent from instruction-following and needs-driven baselines. Our work offers a structured approach to bridging intrinsic values and grounded behavior for autonomous agents.