2017/03/02 by Ali Ghadirzadeh, Ghadirzadeh, Ali, Atsuto Maki +6 · 1 citation
Computer Science · Engineering · #FOS: Computer and information sciences #Muscle activation and electromyography studies #Reinforcement Learning in Robotics #Robot Manipulation and Learning #Robotics (cs.RO) #cs.RO
paper · pdf · doi:10.48550/arxiv.1703.00727
This work is submitted to IEEE/RSJ International Conference on Intelligent Robots and Systems 2017 (IROS2017)
arxiv created 2017/03/02 · openalex publication_date 2017/03/02 · arxiv updated 2017/03/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Skilled robot task learning is best implemented by predictive action policies due to the inherent latency of sensorimotor processes. However, training such predictive policies is challenging as it involves finding a trajectory of motor activations for the full duration of the action. We propose a data-efficient deep predictive policy training (DPPT) framework with a deep neural network policy architecture which maps an image observation to a sequence of motor activations. The architecture consists of three sub-networks referred to as the perception, policy and behavior super-layers. The perception and behavior super-layers force an abstraction of visual and motor data trained with synthetic and simulated training samples, respectively. The policy super-layer is a small sub-network with fewer parameters that maps data in-between the abstracted manifolds. It is trained for each task using methods for policy search reinforcement learning. We demonstrate the suitability of the proposed architecture and learning framework by training predictive policies for skilled object grasping and ball throwing on a PR2 robot. The effectiveness of the method is illustrated by the fact that these tasks are trained using only about 180 real robot attempts with qualitative terminal rewards.