Contact Skill Imitation Learning for Robot-Independent Assembly Programming
On the map: Offline RL and imitation learning from data → Robot learning from demonstration
Abstract (arXiv)
Robotic automation is a key driver for the advancement of technology. The skills of human workers, however, are difficult to program and seem currently unmatched by technical systems. In this work we present a data-driven approach to extract and learn robot-independent contact skills from human demonstrations in simulation environments, using a Long Short Term Memory (LSTM) network. Our model learns to generate error-correcting sequences of forces and torques in task space from object-relative motion, which industrial robots carry out through a Cartesian force control scheme on the real setup. This scheme uses forward dynamics computation of a virtually conditioned twin of the manipulator to solve the inverse kinematics problem. We evaluate our methods with an assembly experiment, in which our algorithm handles part tilting and jamming in order to succeed. The results show that the skill is robust towards localization uncertainty in task space and across different joint configurations of the robot. With our approach, non-experts can easily program force-sensitive assembly tasks in a robot-independent way.
Key facts. Cited by 4 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 6 of its 45 references that are in the map. First posted on arXiv 17 Aug 2019.
Builds on
6 of 45 references.
References · 6
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2014Show and Tell: A Neural Image Caption Generatorcited by 879
- 2015End-to-End Training of Deep Visuomotor Policiescited by 875
- 2017Deep Reinforcement Learning for High Precision Assembly Taskscited by 39
- 2015Learning Contact-Rich Manipulation Skills with Guided Policy Searchcited by 38
- 2018Learning Robotic Assembly from CADcited by 21
Built on by
Citing papers per year: 2023: 2; 2024: 0; 2025: 2.
4 papers.
- 2025A Survey on Imitation Learning for Contact-Rich Tasks in Roboticscited by 14
- 2025Geometric Contact Flows: Contactomorphisms for Dynamics and Controlcited by 2
- 2023Learning Human-Inspired Force Strategies for Robotic Assemblycited by 1
- 2023PolyFit: A Peg-in-hole Assembly Framework for Unseen Polygon Shapes via Sim-to-real Adaptationcited by 1
- 2025Geometric Contact Flows: Contactomorphisms for Dynamics and Control22 Jun 2025
- 2025A Survey on Imitation Learning for Contact-Rich Tasks in Robotics16 Jun 2025
- 2023PolyFit: A Peg-in-hole Assembly Framework for Unseen Polygon Shapes via Sim-to-real Adaptation5 Dec 2023
- 2023Learning Human-Inspired Force Strategies for Robotic Assembly22 Mar 2023
Similar, not cited
Close in wording, no citation either way
- 2021Robotic Imitation of Human Assembly Skills Using Hybrid Trajectory and Force Learning
- 2021Imitation and Supervised Learning of Compliance for Robotic Assembly
- 2021Learning Forceful Manipulation Skills from Multi-modal Human Demonstrations
- 2022A System for Imitation Learning of Contact-Rich Bimanual Manipulation Policies
- 2026One-Shot Learning from Demonstration of Contact-Rich Robotic Manipulation by Identifying Physical Interactions
- 2026Contact-Aware Imitation Learning Through Contact Factorization
- 2025SHaRe-RL: Structured, Interactive Reinforcement Learning for Contact-Rich Industrial Assembly Tasks
- 2020Residual Learning from Demonstration: Adapting DMPs for Contact-rich Manipulation