Experience Reuse with Probabilistic Movement Primitives

08/11/2019
by   Svenja Stark, et al.
0

Acquiring new robot motor skills is cumbersome, as learning a skill from scratch and without prior knowledge requires the exploration of a large space of motor configurations. Accordingly, for learning a new task, time could be saved by restricting the parameter search space by initializing it with the solution of a similar task. We present a framework which is able of such knowledge transfer from already learned movement skills to a new learning task. The framework combines probabilistic movement primitives with descriptions of their effects for skill representation. New skills are first initialized with parameters inferred from related movement primitives and thereafter adapted to the new task through relative entropy policy search. We compare two different transfer approaches to initialize the search space distribution with data of known skills with a similar effect. We show the different benefits of the two knowledge transfer approaches on an object pushing task for a simulated 3-DOF robot. We can show that the quality of the learned skills improves and the required iterations to learn a new task can be reduced by more than 60 past experiences are utilized.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/28/2022

Bimanual rope manipulation skill synthesis through context dependent correction policy learning from human demonstration

Learning from demonstration (LfD) provides a convenient means to equip r...
research
09/17/2021

Search-Based Task Planning with Learned Skill Effect Models for Lifelong Robotic Manipulation

Lifelong-learning robots need to be able to acquire new skills and plan ...
research
03/08/2022

Residual Robot Learning for Object-Centric Probabilistic Movement Primitives

It is desirable for future robots to quickly learn new tasks and adapt l...
research
05/28/2021

Incremental Learning of Probabilistic Movement Primitives (ProMPs) for Human-Robot Cooperation

For a successful deployment of physical Human-Robot Cooperation (pHRC), ...
research
09/28/2022

Constrained Dynamic Movement Primitives for Safe Learning of Motor Skills

Dynamic movement primitives are widely used for learning skills which ca...
research
02/10/2016

Adaptive Skills, Adaptive Partitions (ASAP)

We introduce the Adaptive Skills, Adaptive Partitions (ASAP) framework t...
research
10/14/2022

Learning Skills from Demonstrations: A Trend from Motion Primitives to Experience Abstraction

The uses of robots are changing from static environments in factories to...

Please sign up or login with your details

Forgot password? Click here to reset