  Real-time interception of fast-moving objects by robotic arms in dynamic
environments poses a formidable challenge due to the need for rapid reaction
times, often within milliseconds, amidst dynamic obstacles. This paper
introduces a unified control framework to address the above challenge by
simultaneously intercepting dynamic objects and avoiding moving obstacles.
Central to our approach is using diffusion-based variational autoencoder for
motion planning to perform both object interception and obstacle avoidance. We
begin by encoding the high-dimensional temporal information from streaming
events into a two-dimensional latent manifold, enabling the discrimination
between safe and colliding trajectories, culminating in the construction of an
offline densely connected trajectory graph. Subsequently, we employ an extended
Kalman filter to achieve precise real-time tracking of the moving object.
Leveraging a graph-traversing strategy on the established offline dense graph,
we generate encoded robotic motor control commands. Finally, we decode these
commands to enable real-time motion of robotic motors, ensuring effective
obstacle avoidance and high interception accuracy of fast-moving objects.
Experimental validation on both computer simulations and autonomous 7-DoF
robotic arms demonstrates the efficacy of our proposed framework. Results
indicate the capability of the robotic manipulator to navigate around multiple
obstacles of varying sizes and shapes while successfully intercepting
fast-moving objects thrown from different angles by hand. Complete video
demonstrations of our experiments can be found in
https://sites.google.com/view/multirobotskill/home.
