RMMI：基于隐式神经地图的反应式移动操作
RMMI: Reactive Mobile Manipulation using an Implicit Neural Map
AI总结：
针对现有反应式控制依赖简化环境几何表示、在杂乱环境中性能受限的问题，提出基于神经SDF的RMMI反应式控制框架，通过二次规划联合优化基座与机械臂运动，仿真和实实验证其伸手任务成功率提升25%。
摘要：
在复杂的家庭和工业环境中运行的移动操作机器人必须在避开障碍物的同时有效协调其基座和机械臂的运动。当前的反应式控制方法虽能很好地实现这种协调，但它们依赖于简化且理想化的环境几何表示来避免碰撞，这限制了其在杂乱环境中的性能。为解决这一问题，我们提出了RMMI（基于隐式神经地图的反应式移动操作），这是一种反应式控制框架，利用神经有符号距离场（SDFs）提供连续且可微的环境几何表示的能力。RMMI构建了一个二次规划问题，联合优化机器人基座和机械臂的运动，最大化可操作性，并通过一组不等式约束避免碰撞。这些约束通过查询SDF，获取机器人上大量采样点到最近障碍物的距离和方向来构建。我们在仿真和一系列真实世界实验中评估了RMMI。在杂乱环境中执行伸手任务时，我们观察到成功率提升了25%。更多细节、代码和实验视频请访问https://rmmi.github.io/。
英文摘要：
Mobile manipulator robots operating in complex domestic and industrial environments must effectively coordinate their base and arm motions while avoiding obstacles. While current reactive control methods gracefully achieve this coordination, they rely on simplified and idealised geometric representations of the environment to avoid collisions. This limits their performance in cluttered environments. To address this problem, we introduce RMMI, a reactive control framework that leverages the ability of neural Signed Distance Fields (SDFs) to provide a continuous and differentiable representation of the environment's geometry. RMMI formulates a quadratic program that optimises jointly for robot base and arm motion, maximises the manipulability, and avoids collisions through a set of inequality constraints. These constraints are constructed by querying the SDF for the distance and direction to the closest obstacle for a large number of sampling points on the robot. We evaluate RMMI both in simulation and in a set of real-world experiments. For reaching in cluttered environments, we observe a 25% increase in success rate. For additional details, code, and experiment videos, please visit https://rmmi.github.io/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
