Generating safe motion plans in real-time is a key requirement for deploying robot manipulators to assist humans in collaborative settings. In particular, robots must satisfy strict safety requirements to avoid self-damage or harming nearby humans. Satisfying these requirements is particularly challenging if the robot must also operate in real-time to adjust to changes in its environment.This paper addresses these challenges by proposing Reachability-based Signed Distance Functions (RDFs) as a neural implicit representation for robot safety. RDF, which can be constructed using supervised learning in a tractable fashion, accurately predicts the distance between the swept volume of a robot arm and an obstacle. RDF's inference and gradient computations are fast and scale linearly with the dimension of the system; these features enable its use within a novel real-time trajectory planning framework as a continuous-time collision-avoidance constraint. The planning method using RDF is compared to a variety of state-of-the-art techniques and is demonstrated to successfully solve challenging motion planning tasks for high-dimensional systems faster and more reliably than all tested methods.

翻译：在协作环境中，生成实时安全运动规划是部署机器人操纵器辅助人类的关键要求。具体而言，机器人必须满足严格的安全约束，以避免自损伤或伤害附近人员。若机器人还需实时运行以调整其环境变化，满足这些约束将尤为困难。本文通过提出基于可达性的符号距离函数（RDF）作为机器人安全的神经隐式表示，来应对这些挑战。RDF可通过监督学习以可处理的方式构建，能够准确预测机器人手臂扫掠体积与障碍物之间的距离。RDF的推理和梯度计算速度快，且与系统维度呈线性关系；这些特性使其能够在新型实时轨迹规划框架中作为连续时间避碰约束使用。将使用RDF的规划方法与多种最新技术进行对比，结果表明，该方法在解决高维系统的复杂运动规划任务时，比所有测试方法更快且更可靠。