让四足机械臂规划更安全，通过可达区域优化路径。
RAKOMO: Reachability-Aware K-Order Markov Path Optimization for Quadrupedal Loco-Manipulation
- 用神经网络预测可达范围，融入K阶马尔可夫优化框架
- 在HyQReal机器人上实现快速稳定抓取任务，收敛更快
- 适合需要兼顾运动与操作的复杂四足机器人应用
四足机械臂在执行操作任务时需考虑复杂的运动学约束，以确保安全有效。然而，传统轨迹优化方法常因接触突变带来的混合动力学而受限，且为计算效率忽略腿部限制。本文提出RAKOMO，将K-阶马尔可夫优化（KOMO）与基于可达区域定义的可达性裕度相结合，利用神经网络预测该裕度，并将其嵌入标准KOMO公式中。该方法使梯度驱动的运动规划能快速收敛，适用于非连续系统，有效适配四足机械臂。我们在搭载Kinova Gen3机械臂的HyQReal四足机器人上，通过一系列抓取-放置任务的仿真测试，验证了RAKOMO相较于基线KOMO方法在稳定性与效率上的显著提升。
原文摘要 · Abstract (English)
Legged manipulators, such as quadrupeds equipped with robotic arms, require motion planning techniques that account for their complex kinematic constraints in order to perform manipulation tasks both safely and effectively. However, trajectory optimization methods often face challenges due to the hybrid dynamics introduced by contact discontinuities, and tend to neglect leg limitations during planning for computational reasons. In this work, we propose RAKOMO, a path optimization technique that integrates the strengths of K-Order Markov Optimization (KOMO) with a kinematically-aware criterion based on the reachable region defined as reachability margin. We leverage a neural-network to predict the margin and optimize it by incorporating it in the standard KOMO formulation. This approach enables rapid convergence of gradient-based motion planning -- commonly tailored for continuous systems -- while adapting it effectively to legged manipulators, successfully executing loco-manipulation tasks. We benchmark RAKOMO against a baseline KOMO approach through a set of simulations for pick-and-place tasks with the HyQReal quadruped robot equipped with a Kinova Gen3 robotic arm.
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