用可微分地图加速机器人运动规划，提升生成效率。
Learning Differentiable Reachability Maps for Optimization-based Humanoid Motion Generation
- 用神经网络或SVM学习可微分可达性地图，实现任务空间连续建模。
- 在足部规划、多接触和人机交互任务中，求解速度显著提升。
- 适合需要高效运动规划的复杂人形机器人应用场景。
为降低人形机器人运动生成的计算成本，本文提出一种新的机器人运动学可达性表示方法：可微分可达性地图。该地图是定义在任务空间的标量函数，仅在机器人末端执行器可达区域取正值。其关键特性是关于任务空间坐标的连续性和可微性，可直接作为连续优化中的约束条件用于人形机器人运动规划。我们提出一种从机器人运动学模型生成的末端位姿集合中学习此类地图的方法，使用神经网络或支持向量机作为学习模型。通过将学习得到的可达性地图作为约束，将人形机器人运动生成建模为连续优化问题。实验表明，该方法能高效解决多种运动规划问题，包括步态规划、多接触运动规划以及人形机器人的运载操作规划。
原文摘要 · Abstract (English)
To reduce the computational cost of humanoid motion generation, we introduce a new approach to representing robot kinematic reachability: the differentiable reachability map. This map is a scalar-valued function defined in the task space that takes positive values only in regions reachable by the robot's end-effector. A key feature of this representation is that it is continuous and differentiable with respect to task-space coordinates, enabling its direct use as constraints in continuous optimization for humanoid motion planning. We describe a method to learn such differentiable reachability maps from a set of end-effector poses generated using a robot's kinematic model, using either a neural network or a support vector machine as the learning model. By incorporating the learned reachability map as a constraint, we formulate humanoid motion generation as a continuous optimization problem. We demonstrate that the proposed approach efficiently solves various motion planning problems, including footstep planning, multi-contact motion planning, and loco-manipulation planning for humanoid robots.
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