Parijat Dewangan

TL;DR: The proposed methodology to generate joint-space trajectories of stable configurations for solving inverse kinematics using Deep Reinforcement Learning (RL) is based on the idea of exploring the entire configuration space of the robot and learning the best possible solutions using Deep Deterministic Policy Gradient (DDPG).

TL;DR: In this paper, the authors proposed a methodology to generate joint-space trajectories of stable configurations for solving inverse kinematics using deep reinforcement learning (RL) based on the idea of exploring the entire configuration space of the robot and learning the best possible solutions using Deep Deterministic Policy Gradient (DDPG) The proposed strategy was evaluated on the highly articulated upper body of a humanoid model with 27 degree of freedom.

TL;DR: The proposed framework is based on differential policy gradients and can accommodate multi-task learning in a single actor-critic network and a simple heuristic in the differential policy gradient update is proposed to further improve the learning.

TL;DR: The model of a Humanoid robot inspired by Poppy, modified for heavier load capacity and the balancing of humanoid in multiple work environments, modified in order to use MX-64 servos with more torque capacity than MX-28 servos.

TL;DR: This paper proposes DiGrad (Differential Gradients), a new RL framework for multi-task learning in manipulators and shows how this framework can be adopted to learn dual arm coordination in a 27 degrees of freedom (DOF) humanoid robot with articulated spine.