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

arXiv: https://arxiv.org/abs/2110.14817 · HTML: https://mlsanity.com/paper/2110.14817/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Robot learning from demonstration, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 28 Oct 2021.

## Placed in

- Offline RL and imitation learning from data / [Robot learning from demonstration](https://mlsanity.com/offlinerl/robot-learning-from-demonstration/)

## Builds on (most cited first, 7 of 7)

- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)
- [Learning Reward Functions by Integrating Human Demonstrations and Preferences](https://mlsanity.com/paper/1906.08928/) (2019)
- [Kernelized Movement Primitives](https://mlsanity.com/paper/1708.08638/) (2017)
- [Benchmark for Skill Learning from Demonstration: Impact of User Experience, Task Complexity, and Start Configuration on Performance](https://mlsanity.com/paper/1911.02725/) (2019)
- [Skill Acquisition via Automated Multi-Coordinate Cost Balancing](https://mlsanity.com/paper/1903.11725/) (2019)
- [Efficient Robotic Task Generalization Using Deep Model Fusion Reinforcement Learning](https://mlsanity.com/paper/1912.05205/) (2019)
- [Learning from Successful and Failed Demonstrations via Optimization](https://mlsanity.com/paper/2107.11918/) (2021)

## Built on by (most cited first, 2 of 2)

- [Robot Learning from Demonstration Using Elastic Maps](https://mlsanity.com/paper/2208.02207/) (2022)
- [Auto-LfD: Towards Closing the Loop for Learning from Demonstrations](https://mlsanity.com/paper/2310.09791/) (2023)

## Abstract (arXiv)

Learning from Demonstration (LfD) algorithms enable humans to teach new skills to robots through demonstrations. The learned skills can be robustly reproduced from the identical or near boundary conditions (e.g., initial point). However, when generalizing a learned skill over boundary conditions with higher variance, the similarity of the reproductions changes from one boundary condition to another, and a single LfD representation cannot preserve a consistent similarity across a generalization region. We propose a novel similarity-aware framework including multiple LfD representations and a similarity metric that can improve skill generalization by finding reproductions with the highest similarity values for a given boundary condition. Given a demonstration of the skill, our framework constructs a similarity region around a point of interest (e.g., initial point) by evaluating individual LfD representations using the similarity metric. Any point within this volume corresponds to a representation that reproduces the skill with the greatest similarity. We validate our multi-representational framework in three simulated and four sets of real-world experiments using a physical 6-DOF robot. We also evaluate 11 different similarity metrics and categorize them according to their biases in 286 simulated experiments.