Improving dual-arm assembly by master-slave compliance
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Abstract (arXiv)
In this paper we show how different choices regarding compliance affect a dual-arm assembly task. In addition, we present how the compliance parameters can be learned from a human demonstration. Compliant motions can be used in assembly tasks to mitigate pose errors originating from, for example, inaccurate grasping. We present analytical background and accompanying experimental results on how to choose the center of compliance to enhance the convergence region of an alignment task. Then we present the possible ways of choosing the compliant axes for accomplishing alignment in a scenario where orientation error is present. We show that an earlier presented Learning from Demonstration method can be used to learn motion and compliance parameters of an impedance controller for both manipulators. The learning requires a human demonstration with a single teleoperated manipulator only, easing the execution of demonstration and enabling usage of manipulators at difficult locations as well. Finally, we experimentally verify our claim that having both manipulators compliant in both rotation and translation can accomplish the alignment task with less total joint motions and in shorter time than moving one manipulator only. In addition, we show that the learning method produces the parameters that achieve the best results in our experiments.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Feb 2019.
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Citing papers per year: 2019: 1; 2020: 0; 2021: 1; 2022: 0; 2023: 0; 2024: 0; 2025: 0; 2026: 1.
3 papers.
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- 2021Imitation and Supervised Learning of Compliance for Robotic Assembly
- 2018Imitation learning-based framework for learning 6-D linear compliant motions
- 2026Compliance for Free: Learning Identifiable Impedance via Bilateral Teleoperation ↗
- 2021Robotic Imitation of Human Assembly Skills Using Hybrid Trajectory and Force Learning
- 2019Contact Skill Imitation Learning for Robot-Independent Assembly Programming
- 2021Learning Forceful Manipulation Skills from Multi-modal Human Demonstrations
- 2026Impedance Cloning: Learning Equilibrium Point Parameters for Contact-Rich Manipulation ↗
- 2022Generalizable Human-Robot Collaborative Assembly Using Imitation Learning and Force Control