To control how a robot moves, motion planning algorithms must compute paths in high-dimensional state spaces while accounting for physical constraints related to motors and joints, generating smooth and stable motions, avoiding obstacles, and preventing collisions. A motion planning algorithm must therefore balance competing demands, and should ideally incorporate uncertainty to handle noise, model errors, and facilitate deployment in complex environments. To address these issues, we introduce a framework for robot motion planning based on variational Gaussian processes, which unifies and generalizes various probabilistic-inference-based motion planning algorithms, and connects them with optimization-based planners. Our framework provides a principled and flexible way to incorporate equality-based, inequality-based, and soft motion-planning constraints during end-to-end training, is straightforward to implement, and provides both interval-based and Monte-Carlo-based uncertainty estimates. We conduct experiments using different environments and robots, comparing against baseline approaches based on the feasibility of the planned paths, and obstacle avoidance quality. Results show that our proposed approach yields a good balance between success rates and path quality.

翻译：为了控制机器人的运动，运动规划算法必须在高维状态空间中计算路径，同时考虑与电机和关节相关的物理约束，生成平滑稳定的运动，避开障碍物并防止碰撞。因此，运动规划算法必须平衡相互竞争的需求，理想情况下还应纳入不确定性以处理噪声、模型误差，并便于在复杂环境中部署。为解决这些问题，我们提出了一种基于变分高斯过程的机器人运动规划框架，该框架统一并泛化了多种基于概率推理的运动规划算法，并将其与基于优化的规划器联系起来。我们的框架提供了一种原则性且灵活的方式，在端到端训练过程中融入等式约束、不等式约束和软运动规划约束，实现简便，并提供基于区间和基于蒙特卡洛的不确定性估计。我们使用不同环境和机器人进行了实验，与基于规划路径可行性和避障质量的基线方法进行了比较。结果表明，我们提出的方法在成功率和路径质量之间取得了良好的平衡。