Learning from Successful and Failed Demonstrations via Optimization

07/26/2021
by   Brendan Hertel, et al.
0

Learning from Demonstration (LfD) is a popular approach that allows humans to teach robots new skills by showing the correct way(s) of performing the desired skill. Human-provided demonstrations, however, are not always optimal and the teacher usually addresses this issue by discarding or replacing sub-optimal (noisy or faulty) demonstrations. We propose a novel LfD representation that learns from both successful and failed demonstrations of a skill. Our approach encodes the two subsets of captured demonstrations (labeled by the teacher) into a statistical skill model, constructs a set of quadratic costs, and finds an optimal reproduction of the skill under novel problem conditions (i.e. constraints). The optimal reproduction balances convergence towards successful examples and divergence from failed examples. We evaluate our approach through several 2D and 3D experiments in real-world using a UR5e manipulator arm and also show that it can reproduce a skill from only failed demonstrations. The benefits of exploiting both failed and successful demonstrations are shown through comparison with two existing LfD approaches. We also compare our approach against an existing skill refinement method and show its capabilities in a multi-coordinate setting.

READ FULL TEXT
research
08/01/2018

Learning Generalizable Robot Skills from Demonstrations in Cluttered Environments

Learning from Demonstration (LfD) is a popular approach to endowing robo...
research
03/27/2019

Skill Acquisition via Automated Multi-Coordinate Cost Balancing

We propose a learning framework, named Multi-Coordinate Cost Balancing (...
research
10/28/2021

Similarity-Aware Skill Reproduction based on Multi-Representational Learning from Demonstration

Learning from Demonstration (LfD) algorithms enable humans to teach new ...
research
03/31/2021

Ergodic imitation: Learning from what to do and what not to do

With growing access to versatile robotics, it is beneficial for end user...
research
08/03/2022

Robot Learning from Demonstration Using Elastic Maps

Learning from Demonstration (LfD) is a popular method of reproducing and...
research
07/09/2019

Towards Orientation Learning and Adaptation in Cartesian Space

As a promising branch in robotics, imitation learning emerges as an impo...
research
05/04/2023

Confidence-Based Skill Reproduction Through Perturbation Analysis

Several methods exist for teaching robots, with one of the most prominen...

Please sign up or login with your details

Forgot password? Click here to reset