Dynamically Avoiding Amorphous Obstacles with Topological Manifold Learning and Deep Autoencoding

To achieve conflict-free human-machine collaborations, robotic agents need to skillfully avoid continuously moving obstacles while achieving collective objectives. Sometimes, these obstacles can even change their 3D shapes and forms simultaneously, hence being "amorphous". To this end, this paper formulates the problem of Dynamic Amorphous Obstacle Avoidance (DAO-A), where a robotic arm can dexterously avoid dynamically generated obstacles that constantly change their trajectories and their 3D forms. Specifically, we introduce a novel control strategy for robotic arms that leverages both topological manifold learning and latest deep learning advancements. We test our learning framework, using a 7-DoF robotic manipulator, in both simulation and physical experiments, where the robot satisfactorily learns and synthesizes realistic skills avoiding previously-unseen obstacles, while generating novel movements to achieve pre-defined motion objectives. Most notably, our learned methodology, once finalized, for a given robotic manipulator, can avoid any number of 3D obstacles with arbitrary and unseen moving trajectories, therefore it is universal, versatile, and completely reusable