mlsanityData to 14 Oct 2026

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

arXiv 2310.09791 ↗First posted 15 Oct 2023cs.ROcited by 0

On the map: Offline RL and imitation learning from data → Robot learning from demonstration

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.

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.

Builds on

7 of 19 references.

Closest ancestors · 2

Same direction, newest first

  1. 2021Similarity-Aware Skill Reproduction based on Multi-Representational Learning from Demonstration28 Oct
  2. 2019A Linearly Constrained Nonparametric Framework for Imitation Learning15 Sep

Other references · 5

  1. 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,849
  2. 2018A Tutorial on Bayesian Optimizationcited by 744
  3. 2022Interactive Imitation Learning in Robotics: A Surveycited by 55
  4. 2017Kernelized Movement Primitivescited by 37
  5. 2022Learning Stable Robotic Skills on Riemannian Manifoldscited by 10

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2023Learning from Few Demonstrations with Frame-Weighted Motion Generation
  2. 2018Learning Generalizable Robot Skills from Demonstrations in Cluttered Environments
  3. 2021Learning from Successful and Failed Demonstrations via Optimization
  4. 2022Quantifying Demonstration Quality for Robot Learning and Generalization
  5. 2025An Alignment-Based Approach to Learning Motions from Demonstrations
  6. 2018Auto-conditioned Recurrent Mixture Density Networks for Learning Generalizable Robot Skills
  7. 2023Programming-by-Demonstration for Long-Horizon Robot Tasks
  8. 2024Stochastic Trajectory Optimization for Robotic Skill Acquisition From a Suboptimal Demonstration