# Auto-LfD: Towards Closing the Loop for Learning from Demonstrations

arXiv: https://arxiv.org/abs/2310.09791 · HTML: https://mlsanity.com/paper/2310.09791/

## Key facts

- No citing papers in the map yet (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 15 Oct 2023.

## 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)

- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [Interactive Imitation Learning in Robotics: A Survey](https://mlsanity.com/paper/2211.00600/) (2022)
- [Kernelized Movement Primitives](https://mlsanity.com/paper/1708.08638/) (2017)
- [Learning Stable Robotic Skills on Riemannian Manifolds](https://mlsanity.com/paper/2208.13267/) (2022)
- [A Linearly Constrained Nonparametric Framework for Imitation Learning](https://mlsanity.com/paper/1909.07374/) (2019)
- [Similarity-Aware Skill Reproduction based on Multi-Representational Learning from Demonstration](https://mlsanity.com/paper/2110.14817/) (2021)

## Abstract (arXiv)

Over the past few years, there have been numerous works towards advancing the generalization capability of robots, among which learning from demonstrations (LfD) has drawn much attention by virtue of its user-friendly and data-efficient nature. While many LfD solutions have been reported, a key question has not been properly addressed: how can we evaluate the generalization performance of LfD? For instance, when a robot draws a letter that needs to pass through new desired points, how does it ensure the new trajectory maintains a similar shape to the demonstration? This question becomes more relevant when a new task is significantly far from the demonstrated region. To tackle this issue, a user often resorts to manual tuning of the hyperparameters of an LfD approach until a satisfactory trajectory is attained. In this paper, we aim to provide closed-loop evaluative feedback for LfD and optimize LfD in an automatic fashion. Specifically, we consider dynamical movement primitives (DMP) and kernelized movement primitives (KMP) as examples and develop a generic optimization framework capable of measuring the generalization performance of DMP and KMP and auto-optimizing their hyperparameters without any human inputs. Evaluations including a peg-in-hole task and a pushing task on a real robot evidence the applicability of our framework.