Constrained-Space Optimization and Reinforcement Learning for Complex Tasks

04/01/2020
by   Ya-Yen Tsai, et al.
0

Learning from Demonstration is increasingly used for transferring operator manipulation skills to robots. In practice, it is important to cater for limited data and imperfect human demonstrations, as well as underlying safety constraints. This paper presents a constrained-space optimization and reinforcement learning scheme for managing complex tasks. Through interactions within the constrained space, the reinforcement learning agent is trained to optimize the manipulation skills according to a defined reward function. After learning, the optimal policy is derived from the well-trained reinforcement learning agent, which is then implemented to guide the robot to conduct tasks that are similar to the experts' demonstrations. The effectiveness of the proposed method is verified with a robotic suturing task, demonstrating that the learned policy outperformed the experts' demonstrations in terms of the smoothness of the joint motion and end-effector trajectories, as well as the overall task completion time.

READ FULL TEXT

page 1

page 4

page 5

page 7

research
02/13/2021

Learning Variable Impedance Control via Inverse Reinforcement Learning for Force-Related Tasks

Many manipulation tasks require robots to interact with unknown environm...
research
03/29/2023

Learning Complicated Manipulation Skills via Deterministic Policy with Limited Demonstrations

Combined with demonstrations, deep reinforcement learning can efficientl...
research
04/12/2023

Exploiting Symmetry and Heuristic Demonstrations in Off-policy Reinforcement Learning for Robotic Manipulation

Reinforcement learning demonstrates significant potential in automatical...
research
01/24/2023

Constrained Reinforcement Learning for Dexterous Manipulation

Existing learning approaches to dexterous manipulation use demonstration...
research
11/13/2020

Learning Object Manipulation Skills via Approximate State Estimation from Real Videos

Humans are adept at learning new tasks by watching a few instructional v...
research
03/25/2020

Adaptive Conditional Neural Movement Primitives via Representation Sharing Between Supervised and Reinforcement Learning

Learning by Demonstration provides a sample efficient way to equip robot...
research
10/03/2022

Hierarchical reinforcement learning for in-hand robotic manipulation using Davenport chained rotations

End-to-end reinforcement learning techniques are among the most successf...

Please sign up or login with your details

Forgot password? Click here to reset