Empowermentと予測制御の統合フレームワーク
A Unified Framework for Empowerment and Predictive Control
探査と行動に同一のポリシーを用いてEmpowermentを定式化し、標準的なMPCソルバで最適化可能にすることで、タスクコストとの組み合わせが成功率を向上させることを示した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
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著者: Wooyoung Chung, Tristan Shah, Volodymyr Makarenko, Stas Tiomkin
分類: cs.RO
原文アブストラクト
Sampling-based model predictive control (MPC) is a powerful approach to trajectory optimization, but its performance depends on an informative cost function that often requires substantial domain knowledge to design. An alternative is to derive objectives directly from the system dynamics. Empowerment, defined as the channel capacity between an agent's inputs and its future state, provides such a goal-agnostic objective and has been shown to produce useful behaviors across a range of domains. However, existing empowerment-based controllers have two key limitations: empowerment is estimated using a virtual probing policy distinct from the executed control policy, obscuring its connection to the resulting behavior; and its computation typically requires second-order derivatives of the dynamics, limiting compatibility with standard MPC methods. We formulate empowerment using a single policy for both probing and acting, directly linking the empowerment objective to the executed behavior. The resulting objective requires only first-order derivatives and can be optimized with standard MPC solvers, either alone or in combination with a task-specific cost. We evaluate the proposed approach on classical control tasks and show that combining empowerment with task costs matches or exceeds the success rate achieved by either objective alone. Our formulation provides a practical bridge between standard MPC and intrinsically motivated control.
