Modular Neural Network Policies for Learning In-Flight Object Catching with a Robot Hand-Arm System
Wenbin Hu,Fernando Acero,Eleftherios Triantafyllidis,Zhaocheng Liu,Zhibin Li,Wenbin Hu,Fernando Acero,Eleftherios Triantafyllidis,Zhaocheng Liu,Zhibin Li
We present a modular framework designed to enable a robot hand-arm system to learn how to catch flying objects, a task that requires fast, reactive, and accurately-timed robot motions. Our framework consists of five core modules: (i) an object state estimator that learns object trajectory prediction, (ii) a catching pose quality network that learns to score and rank object poses for catching, (iii...
