DAMON leverages manifold learning and variational autoencoding to achieve obstacle avoidance, allowing for motion planning through adaptive graph traversal in a pre-learned low-dimensional hierarchically-structured manifold graph that captures intricate motion dynamics between a robotic arm and its obstacles. This versatile and reusable approach is applicable to various collaboration scenarios. The primary advantage of DAMON is its ability to embed information in a low-dimensional graph, eliminating the need for repeated computation required by current sampling-based methods. As a result, it offers faster and more efficient motion planning with significantly lower computational overhead and memory footprint. In summary, DAMON is a breakthrough methodology that addresses the challenge of dynamic obstacle avoidance in robotic systems and offers a promising solution for safe and efficient human-robot collaboration. Our approach has been experimentally validated on a 7-DoF robotic manipulator in both simulation and physical settings. DAMON enables the robot to learn and generate skills for avoiding previously-unseen obstacles while achieving predefined objectives. We also optimize DAMON's design parameters and performance using an analytical framework. Our approach outperforms mainstream methodologies, including RRT, RRT*, Dynamic RRT*, L2RRT, and MpNet, with 40\% more trajectory smoothness and over 65\% improved latency performance, on average.

翻译：DAMON利用流形学习与变分自编码实现障碍回避，通过自适应图遍历在预学习的低维分层结构化流形图中进行运动规划。该流形图能够捕捉机械臂与其障碍物之间复杂的运动动力学特征。这种通用且可复用的方法适用于多种协作场景。DAMON的主要优势在于其能在低维图中嵌入信息，从而避免当前基于采样的方法所需的重复计算。因此，它在显著降低计算开销和内存占用的同时，提供了更快速、更高效的运动规划。总之，DAMON是一项突破性方法，解决了机器人系统中动态障碍回避的挑战，为安全高效的人机协作提供了有前景的解决方案。该方法已在七自由度机械臂的仿真与物理环境中得到实验验证。DAMON使机器人能够学习并生成技能，以规避先前未见过的障碍物，同时实现预设目标。我们还利用分析框架优化了DAMON的设计参数与性能。与主流方法（包括RRT、RRT*、Dynamic RRT*、L2RRT及MpNet）相比，我们的方法平均提高轨迹平滑度40%，延迟性能改善超过65%。