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Learning Variable Impedance Skills from Demonstrations with Passivity Guarantee
Robotics · Submitted 20 Jun 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim The first evaluation demonstrates that the stiffness estimated approach exhibits superior robustness compared to traditional methods when tested on manual datasets, and the second evaluation illustrates that the novel tank based approach is more easily implementable compared to traditional variable impedance control approaches.
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The abstract, sentence by sentence
Robots are increasingly being deployed not only in workplaces but also in households. Effectively execute of manipulation tasks by robots relies on variable impedance control with contact forces. Furthermore, robots should possess adaptive capabilities to handle the considerable variations exhibited by different robotic tasks in dynamic environments, which can be obtained through human demonstrations. This paper presents a learning-from-demonstration framework that integrates force sensing and motion information to facilitate variable impedance control. The proposed approach involves the estimation of full stiffness matrices from human demonstrations, which are then combined with sensed forces and motion information to create a model using the non-parametric method. This model allows the robot to replicate the demonstrated task while also responding appropriately to new task conditions through the use of the state-dependent stiffness profile. Additionally, a novel tank based variable impedance control approach is proposed to ensure passivity by using the learned stiffness. The proposed approach was evaluated using two virtual variable stiffness systems. The first evaluation demonstrates that the stiffness estimated approach exhibits superior robustness compared to traditional methods when tested on manual datasets, and the second evaluation illustrates that the novel tank based approach is more easily implementable compared to traditional variable impedance control approaches.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Robots are increasingly being deployed not only in workplaces but also in households. | background 1.00 | 0.04 | 0.01 | asserted 1.00 |
| s1 | Effectively execute of manipulation tasks by robots relies on variable impedance control with contact forces. | background 1.00 | 0.05 | 0.03 | asserted 1.00 |
| s2 | Furthermore, robots should possess adaptive capabilities to handle the considerable variations exhibited by different robotic tasks in dynamic environments, which can be obtained through human demonstrations. | background 0.97 | 0.05 | 0.03 | asserted 0.91 |
| s3 | This paper presents a learning-from-demonstration framework that integrates force sensing and motion information to facilitate variable impedance control. | contribution 0.97 | 0.89 | 0.09 | asserted 1.00 |
| s4 | The proposed approach involves the estimation of full stiffness matrices from human demonstrations, which are then combined with sensed forces and motion information to create a model using the non-parametric method. | method 0.99 | 0.91 | 0.06 | asserted 1.00 |
| s5 | This model allows the robot to replicate the demonstrated task while also responding appropriately to new task conditions through the use of the state-dependent stiffness profile. | method 0.81 | 0.63 | 0.15 | asserted 0.93 |
| s6 | Additionally, a novel tank based variable impedance control approach is proposed to ensure passivity by using the learned stiffness. | contribution 0.64 | 0.90 | 0.12 | asserted 0.99 |
| s7 | The proposed approach was evaluated using two virtual variable stiffness systems. | method 0.79 | 0.94 | 0.04 | asserted 0.99 |
| s8 | The first evaluation demonstrates that the stiffness estimated approach exhibits superior robustness compared to traditional methods when tested on manual datasets, and the second evaluation illustrates that the novel tank based approach is more easily implementable compared to traditional variable impedance control approaches. | result 0.99 | 0.95 | 0.53 | asserted 1.00 |
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The paper
Original abstract
Robots are increasingly being deployed not only in workplaces but also in households. Effectively execute of manipulation tasks by robots relies on variable impedance control with contact forces. Furthermore, robots should possess adaptive capabilities to handle the considerable variations exhibited by different robotic tasks in dynamic environments, which can be obtained through human demonstrations. This paper presents a learning-from-demonstration framework that integrates force sensing and motion information to facilitate variable impedance control. The proposed approach involves the estimation of full stiffness matrices from human demonstrations, which are then combined with sensed forces and motion information to create a model using the non-parametric method. This model allows the robot to replicate the demonstrated task while also responding appropriately to new task conditions through the use of the state-dependent stiffness profile. Additionally, a novel tank based variable impedance control approach is proposed to ensure passivity by using the learned stiffness. The proposed approach was evaluated using two virtual variable stiffness systems. The first evaluation demonstrates that the stiffness estimated approach exhibits superior robustness compared to traditional methods when tested on manual datasets, and the second evaluation illustrates that the novel tank based approach is more easily implementable compared to traditional variable impedance control approaches.
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How the literature uses it
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Papers citing it
Cites it as background 1 paper
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Classical and learning-based impedance and variable-stiffness control [ref] shape compliant contact by modulating stiffness and damping over time.
UniReflex: Plug-and-Play Force Control for Pretrained Generative Policies via Fast-Slow Reflex · Force-Aware Robot Control · 0.99
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