  Safe and efficient collaboration among multiple robots in unstructured
environments is increasingly critical in the era of Industry 4.0. However,
achieving robust and autonomous collaboration among humans and other robots
requires modern robotic systems to have effective proximity perception and
reactive obstacle avoidance. In this paper, we propose a novel methodology for
reactive whole-body obstacle avoidance that ensures conflict-free robot-robot
interactions even in dynamic environment. Unlike existing approaches based on
Jacobian-type, sampling based or geometric techniques, our methodology
leverages the latest deep learning advances and topological manifold learning,
enabling it to be readily generalized to other problem settings with high
computing efficiency and fast graph traversal techniques. Our approach allows a
robotic arm to proactively avoid obstacles of arbitrary 3D shapes without
direct contact, a significant improvement over traditional industrial cobot
settings. To validate our approach, we implement it on a robotic platform
consisting of dual 6-DoF robotic arms with optimized proximity sensor
placement, capable of working collaboratively with varying levels of
interference. Specifically, one arm performs reactive whole-body obstacle
avoidance while achieving its pre-determined objective, while the other arm
emulates the presence of a human collaborator with independent and potentially
adversarial movements. Our methodology provides a robust and effective solution
for safe human-robot collaboration in non-stationary environments.
