mlsanityData to 14 Oct 2026

Combining Learning from Demonstration with Learning by Exploration to Facilitate Contact-Rich Tasks

arXiv 2103.05904 ↗First posted 10 Mar 2021cs.ROcited by 2

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

Abstract (arXiv)

Collaborative robots are expected to be able to work alongside humans and in some cases directly replace existing human workers, thus effectively responding to rapid assembly line changes. Current methods for programming contact-rich tasks, especially in heavily constrained space, tend to be fairly inefficient. Therefore, faster and more intuitive approaches to robot teaching are urgently required. This work focuses on combining visual servoing based learning from demonstration (LfD) and force-based learning by exploration (LbE), to enable fast and intuitive programming of contact-rich tasks with minimal user effort required. Two learning approaches were developed and integrated into a framework, and one relying on human to robot motion mapping (the visual servoing approach) and one on force-based reinforcement learning. The developed framework implements the non-contact demonstration teaching method based on visual servoing approach and optimizes the demonstrated robot target positions according to the detected contact state. The framework has been compared with two most commonly used baseline techniques, pendant-based teaching and hand-guiding teaching. The efficiency and reliability of the framework have been validated through comparison experiments involving the teaching and execution of contact-rich tasks. The framework proposed in this paper has performed the best in terms of teaching time, execution success rate, risk of damage, and ease of use.

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 8 of its 44 references that are in the map. First posted on arXiv 10 Mar 2021.

Builds on

8 of 44 references.

References · 8

  1. 2015Prioritized Experience Replaycited by 1,099
  2. 2018Residual Reinforcement Learning for Robot Controlcited by 274
  3. 2018Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Taskscited by 154
  4. 2019Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewardscited by 64
  5. 2017Deep Reinforcement Learning for High Precision Assembly Taskscited by 39
  6. 2019Reinforcement Learning on Variable Impedance Controller for High-Precision Robotic Assemblycited by 39
  7. 2020Guided Uncertainty-Aware Policy Optimization: Combining Learning and Model-Based Strategies for Sample-Efficient Policy Learningcited by 25
  8. 2020Center-of-Mass-based Robust Grasp Planning for Unknown Objects Using Tactile-Visual Sensorscited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Reinforcement Learning for Robotic Insertion of Flexible Cables in Industrial Settingscited by 0
  2. 2026One-Shot Multimodal Learning from Demonstration with Force-Constrained Elastic Mapscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026One-Shot Learning from Demonstration of Contact-Rich Robotic Manipulation by Identifying Physical Interactions
  2. 2023Teaching contact-rich tasks from visual demonstrations by constraint extraction ↗
  3. 2026Learning Dexterous Manipulation Using Contact Wrench Guidance From Human Demonstration
  4. 2024Learning from Demonstration Framework for Multi-Robot Systems Using Interaction Keypoints and Soft Actor-Critic Methods
  5. 2023Guided Learning from Demonstration for Robust Transferability
  6. 2022A System for Imitation Learning of Contact-Rich Bimanual Manipulation Policies
  7. 2021Learning Forceful Manipulation Skills from Multi-modal Human Demonstrations
  8. 2019Self-Evaluation in One-Shot Learning from Demonstration of Contact-Intensive Tasks