Learning Planar Reaching in Simulation
Robotic Planar Reaching in the Real World
Learning Table-top Object Reaching with a 7 DoF Robotic Arm from Simulation
- Feasibility analysis on learning vision-based robotic planar reaching using DQNs in simulation.
- Proposed a modular deep Q network architecture for fast and low-cost transfer of visuo-motor policies from simulation to the real world.
- Proposed an end-to-end fine-tuning method using weighted losses to improve hand-eye coordination.
- Proposed a kinematics-based guided policy search method (K-GPS) to speed up Q learning for robotic applications where kinematic models are known.
- Demonstrated in robotic reaching tasks on a real Baxter robot in velocity and position control modes, e.g., table-top object reaching in clutter and planar reaching.
- More investigations are undergoing for semi-supervised and unsupervised transfer from simulation to the real world using adversarial discriminative approaches.