Passivity Based Iterative Learning of Admittance-Coupled Dynamic Movement Primitives for Interaction with Changing Environments

Kramberger, Aljaž; Shahriari, Erfan; Gams, Andrej; Nemec, Bojan; Ude, Aleš; Haddadin, Sami

doi:10.1109/iros.2018.8593647

Cited by 25 publications

(39 citation statements)

References 21 publications

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“…A bunch of work (Cheah and Wang, 1998;Gams et al, 2014;Uemura et al, 2014;Abu-Dakka et al, 2015;Kramberger et al, 2018) that propose to iteratively adjust the impedance rely on the Iterative learning control (ILC) framework (Bristow et al, 2006). ILC assumes that the performance of an agent that repeatedly performs the same task can be improved by learning from past executions.…”

Section: Vilc Via Iterative Learningmentioning

confidence: 99%

“…As discussed in section 4.1 and Van der Schaft ( 2000 ), passivity is a powerful tool to analyze the stability of the interaction with a changing and potentially unknown environment. Kramberger et al ( 2018 ) propose an admittance-based coupling of DMP that allows both trajectory and force tracking in changing environments. The paper introduces the concept of reference power trajectory to describe the target behavior of the system under control—consisting of DMP, robot, and passive environment.…”

Section: Variable Impedance Learning Control (Vilc)mentioning

confidence: 99%

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Variable Impedance Control and Learning—A Review

Abu‐Dakka

Saveriano

2020

Front. Robot. AI

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Robots that physically interact with their surroundings, in order to accomplish some tasks or assist humans in their activities, require to exploit contact forces in a safe and proficient manner. Impedance control is considered as a prominent approach in robotics to avoid large impact forces while operating in unstructured environments. In such environments, the conditions under which the interaction occurs may significantly vary during the task execution. This demands robots to be endowed with online adaptation capabilities to cope with sudden and unexpected changes in the environment. In this context, variable impedance control arises as a powerful tool to modulate the robot's behavior in response to variations in its surroundings. In this survey, we present the state-of-the-art of approaches devoted to variable impedance control from control and learning perspectives (separately and jointly). Moreover, we propose a new taxonomy for mechanical impedance based on variability, learning, and control. The objective of this survey is to put together the concepts and efforts that have been done so far in this field, and to describe advantages and disadvantages of each approach. The survey concludes with open issues in the field and an envisioned framework that may potentially solve them.

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Section: Vilc Via Iterative Learningmentioning

confidence: 99%

Section: Variable Impedance Learning Control (Vilc)mentioning

confidence: 99%

Variable Impedance Control and Learning—A Review

Abu‐Dakka

Saveriano

2020

Front. Robot. AI

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“…When combined with dynamical systems for trajectory generation, impedance control law provides a natural adaptation of the motion pattern to the surface movement [12]. It can also enable compliant and robust control of motion and force simultaneously under surface movement [13][14][15]. In our previous work [15], we only considered interaction with surfaces using a robotic arm equipped with a finger tool; i.e., a rigid non-actuated cylindrical shape tool with a round tip to apply desired forces to surfaces.…”

Section: Introductionmentioning

confidence: 99%

Arm-hand motion-force coordination for physical interactions with non-flat surfaces using dynamical systems: Toward compliant robotic massage

Khoramshahi

Henriks

Naef

et al. 2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

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Many manipulation tasks require coordinated motions for arm and fingers. Complexity increases when the task requires to control for the force at contact against a non-flat surface; This becomes even more challenging when this contact is done on a human. All these challenges are regrouped when one, for instance, massages a human limb. When massaging, the robotic arm is required to continuously adapt its orientation and distance to the limb while the robot fingers exert desired patterns of forces and motion on the skin surface. To address these challenges, we adopt a Dynamical System (DS) approach that offers a unified motion-force control approach and enables to easily coordinate multiple degrees of freedom. As each human limb may slightly differ, we learn a model of the surface using support vector regression (SVR) which enable us to obtain a distance-to-surface mapping. The gradient of this mapping, along with the DS, generates the desired motions for the interaction with the surface. A DS-based impedance control for the robotic fingers allows to control separately for force along the normal direction of the surface while moving in the tangential plane. We validate our approach using the KUKA IIWA robotic arm and Allegro robotic hand for massaging a mannequin arm covered with a skin-like material. We show that our approach allows for 1) reactive motion planning to reach for an unknown surface, 2) following desired motion patterns on the surface, and 3) exerting desired interaction forces profiles. Our results show the effectiveness of our approach; especially the robustness toward uncertainties for shape and the given location of the surface.

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“…impedance parameters online (e.g., inertia, damping, and stiffness) to improve tracking in response to force, position, or velocity measurements [4]- [7]. Set-point adaptation approach improves force tracking by adjusting the impedance set-point (e.g., the reference position) based on force tracking error or on real-time estimation of the environment's change in stiffness [8]- [12]. To compensate the tracking error due to environment uncertainties, many works have also focused on learning control in an iterative manner [13].…”

Section: Introductionmentioning

confidence: 99%

“…These reactivity and adaptability need to be continuous, smooth, and robust toward human highly dynamic behaviour and other uncertainties in the environment. Representing tasks with time-indexed references for position and force profiles is the main drawback in current approaches in achieving fast reactivity toward large disturbances; see [11], [12], [16], [17] as examples where a time-dependent representation of the task is used. In contrast, in a statedependent and time-invariant task representation, interactions with the environment can be captured by changes in the robot's state which can be used in the modification and replanning of the task [18].…”

Section: Introductionmentioning

confidence: 99%

Force Adaptation in Contact Tasks with Dynamical Systems

Amanhoud

Khoramshahi

Bonnesoeur

et al. 2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

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In many tasks such as finishing operations, achieving accurate force tracking is essential. However, uncertainties in the robot dynamics and the environment limit the force tracking accuracy. Learning a compensation model for these uncertainties to reduce the force error is an effective approach to overcome this limitation. However, this approach requires an adaptive and robust framework for motion and force generation. In this paper, we use the time-invariant Dynamical System (DS) framework for force adaptation in contact tasks. We propose to improve force tracking accuracy through online adaptation of a state-dependent force correction model encoded with Radial Basis Functions (RBFs). We evaluate our method with a KUKA LWR IV+ robotic arm. We show its efficiency to reduce the force error to a negligible amount with different target forces and robot velocities. Furthermore, we study the effect of the hyper-parameters and provide a guideline for their selection. We showcase a collaborative cleaning task with a human by integrating our method to previous works to achieve force, motion, and task adaptation at the same time. Thereby, we highlight the benefits of using adaptive force control in real-world environments where we need reactive and adaptive behaviours in response to interactions with the environment.

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Passivity Based Iterative Learning of Admittance-Coupled Dynamic Movement Primitives for Interaction with Changing Environments

Cited by 25 publications

References 21 publications

Variable Impedance Control and Learning—A Review

Variable Impedance Control and Learning—A Review

Arm-hand motion-force coordination for physical interactions with non-flat surfaces using dynamical systems: Toward compliant robotic massage

Force Adaptation in Contact Tasks with Dynamical Systems

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