用自由能门控机制组合简单动作，让机器人在障碍环境中自主导航。
Free-Gate: Planning, Control And Policy Composition via Free Energy Gating
- 通过最小化自由能的门控机制组合控制原语，实现智能决策。
- 即使环境非线性、随机且时变，优化问题仍保持凸性，保证解的可靠性。
- 适用于仅有基础动作模块的机器人系统，适合强化学习与具身智能研究者。
本文研究如何最优地组合一组基本控制原语以完成规划与控制任务。为此，提出一种基于自由能计算的规划与控制模型——Free-Gate。该模型通过门控机制组合控制原语，以最小化变分自由能。该组合问题被建模为有限时域最优控制问题，并证明即使代价函数在状态/动作上非凸，且环境为非线性、随机、非平稳的情况下，该问题仍保持凸性。我们开发了一种算法以计算最优原语组合，并通过仿真和硬件实验验证了其有效性。实验表明，在仅具备简单电机原语（单独无法完成任务）的条件下，机器人仍能成功导航至目标位置。
原文摘要 · Abstract (English)
We consider the problem of optimally composing a set of primitives to tackle planning and control tasks. To address this problem, we introduce a free energy computational model for planning and control via policy composition: Free-Gate. Within Free-Gate, control primitives are combined via a gating mechanism that minimizes variational free energy. This composition problem is formulated as a finite-horizon optimal control problem, which we prove remains convex even when the cost is not convex in states/actions and the environment is nonlinear, stochastic and non-stationary. We develop an algorithm that computes the optimal primitives composition and demonstrate its effectiveness via in-silico and hardware experiments on an application involving robot navigation in an environment with obstacles. The experiments highlight that Free-Gate enables the robot to navigate to the destination despite only having available simple motor primitives that, individually, could not fulfill the task.
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