Real2Sim or Sim2Real: Robotics Visual Insertion using Deep Reinforcement Learning and Real2Sim Policy Adaptation

by   Yiwen Chen, et al.

Reinforcement learning has shown a wide usage in robotics tasks, such as insertion and grasping. However, without a practical sim2real strategy, the policy trained in simulation could fail on the real task. There are also wide researches in the sim2real strategies, but most of those methods rely on heavy image rendering, domain randomization training, or tuning. In this work, we solve the insertion task using a pure visual reinforcement learning solution with minimum infrastructure requirement. We also propose a novel sim2real strategy, Real2Sim, which provides a novel and easier solution in policy adaptation. We discuss the advantage of Real2Sim compared with Sim2Real.


page 2

page 6

page 10


Improving Assistive Robotics with Deep Reinforcement Learning

Assistive Robotics is a class of robotics concerned with aiding humans i...

On the Verge of Solving Rocket League using Deep Reinforcement Learning and Sim-to-sim Transfer

Autonomously trained agents that are supposed to play video games reason...

Wield: Systematic Reinforcement Learning With Progressive Randomization

Reinforcement learning frameworks have introduced abstractions to implem...

Deep Reinforcement Learning for Single-Shot Diagnosis and Adaptation in Damaged Robots

Robotics has proved to be an indispensable tool in many industrial as we...

On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning

The lottery ticket hypothesis questions the role of overparameterization...

OmniDRL: Robust Pedestrian Detection using Deep Reinforcement Learning on Omnidirectional Cameras

Pedestrian detection is one of the most explored topics in computer visi...

Deep Reinforcement Learning with Linear Quadratic Regulator Regions

Practitioners often rely on compute-intensive domain randomization to en...

Please sign up or login with your details

Forgot password? Click here to reset