Model predictive control (MPC) is a popular approach for trajectory optimization in practical robotics applications. MPC policies can optimize trajectory parameters under kinodynamic and safety constraints and provide guarantees on safety, optimality, generalizability, interpretability, and explainability. However, some behaviors are complex and it is difficult to hand-craft an MPC objective function. A special class of MPC policies called Learnable-MPC addresses this difficulty using imitation learning from expert demonstrations. However, they require the demonstrator and the imitator agents to be identical which is hard to satisfy in many real world applications of robotics. In this paper, we address the practical problem of training Learnable-MPC policies when the demonstrator and the imitator do not share the same dynamics and their state spaces may have a partial overlap. We propose a novel approach that uses a generative adversarial network (GAN) to minimize the Jensen-Shannon divergence between the state-trajectory distributions of the demonstrator and the imitator. We evaluate our approach on a variety of simulated robotics tasks of DeepMind Control suite and demonstrate the efficacy of our approach at learning the demonstrator's behavior without having to copy their actions.

翻译：模型预测控制（MPC）是实际机器人应用中轨迹优化的常用方法。MPC策略可在运动动力学与安全性约束下优化轨迹参数，并提供关于安全性、最优性、泛化性、可解释性与可说明性的保证。然而，某些复杂行为难以通过人工设计MPC目标函数实现。一类称为"可学习MPC"的专用策略通过模仿专家演示来克服这一困难，但其要求演示者与模仿者智能体具有相同的动力学特性，这一条件在诸多实际机器人应用中难以满足。本文针对演示者与模仿者动力学特性不同且状态空间可能部分重叠时，如何训练可学习MPC策略的实际问题展开研究。我们提出一种创新方法，利用生成对抗网络（GAN）最小化演示者与模仿者状态轨迹分布之间的詹森-香农散度。在DeepMind控制套件的多种仿真机器人任务中，我们评估了该方法，证明其无需复制演示者的动作即可有效习得演示者的行为。