  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.
