让机器人在不确定环境中安全高效迭代执行任务
SIT-LMPC: Safe Information-Theoretic Learning Model Predictive Control for Iterative Tasks
- 基于信息论的模型预测控制，融合过往轨迹学价值函数
- 自适应惩罚机制确保约束满足，性能随迭代持续提升
- 适合高并发计算，硬件实测验证了安全与鲁棒性
在复杂不确定环境中执行迭代任务的机器人需要兼顾鲁棒性、安全性和高性能的控制策略。本文提出一种针对离散时间非线性随机系统的受限无限时域最优控制问题的安全信息论学习模型预测控制（SIT-LMPC）算法。设计了一种基于信息论模型预测控制的迭代控制框架，通过前序迭代轨迹利用归一化流学习价值函数，相比高斯先验能更丰富地建模不确定性。开发自适应惩罚方法，在保证安全性的同时平衡最优性。该算法专为图形处理器上的高度并行执行设计，支持高效的实时优化。基准仿真与硬件实验表明，SIT-LMPC 在迭代过程中持续提升系统性能，并稳健满足系统约束。
原文摘要 · Abstract (English)
Robots executing iterative tasks in complex, uncertain environments require control strategies that balance robustness, safety, and high performance. This paper introduces a safe information-theoretic learning model predictive control (SIT-LMPC) algorithm for iterative tasks. Specifically, we design an iterative control framework based on an information-theoretic model predictive control algorithm to address a constrained infinite-horizon optimal control problem for discrete-time nonlinear stochastic systems. An adaptive penalty method is developed to ensure safety while balancing optimality. Trajectories from previous iterations are utilized to learn a value function using normalizing flows, which enables richer uncertainty modeling compared to Gaussian priors. SIT-LMPC is designed for highly parallel execution on graphics processing units, allowing efficient real-time optimization. Benchmark simulations and hardware experiments demonstrate that SIT-LMPC iteratively improves system performance while robustly satisfying system constraints.
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