Online Learning of Feed-Forward Models for Task-Space Variable Impedance Control

Mathew, Michael; Sidhik, Saif; Sridharan, Mohan; Azad, Muhammad Ajmal; Hayashi, Atsushi; Wyatt, Jeremy L.

doi:10.1109/humanoids43949.2019.9035063

Cited by 7 publications

(10 citation statements)

References 26 publications

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“…Therefore, using learning-based model predictive control, we aim to learn a discrete-time state-space representation of the task dynamics as

Here

is a potentially non-linear target function describing the system dynamics. Based on the intuition of the state-space representation of the dynamics for linear systems and findings in Mathew et al (2019) , we incorporate the action (

) taken at each time step into the learned model.…”

Section: Methodsmentioning

confidence: 99%

“…Here F(x t , f r,t ) is a potentially non-linear target function describing the system dynamics. Based on the intuition of the state-space representation of the dynamics for linear systems and findings in Mathew et al (2019), we incorporate the action (f r,t ) taken at each time step into the learned model.…”

Section: Control System Architecturementioning

confidence: 99%

“…With these limitations in mind, the introduction of learning-based methods has presented considerable advantages in flexibility. In Mathew et al (2019) a feed-forward Gaussian mixture dynamics model was used to guide a variable impedance controller, showing the capability to adapt to a range of materials with variable stiffness, viscosity and surface friction. For learning-based MPC, similar works have focused on learning a dynamic model of the cutting process for a range of food articles ( Lenz et al, 2015 ; Mitsioni et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

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Vision-Guided MPC for Robotic Path Following Using Learned Memory-Augmented Model

Rastegarpanah

Hathaway

2021

Front. Robot. AI

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The control of the interaction between the robot and environment, following a predefined geometric surface path with high accuracy, is a fundamental problem for contact-rich tasks such as machining, polishing, or grinding. Flexible path-following control presents numerous applications in emerging industry fields such as disassembly and recycling, where the control system must adapt to a range of dissimilar object classes, where the properties of the environment are uncertain. We present an end-to-end framework for trajectory-independent robotic path following for contact-rich tasks in the presence of parametric uncertainties. We formulate a combination of model predictive control with image-based path planning and real-time visual feedback, based on a learned state-space dynamic model. For modeling the dynamics of the robot-environment system during contact, we introduce the application of the differentiable neural computer, a type of memory augmented neural network (MANN). Although MANNs have been as yet unexplored in a control context, we demonstrate a reduction in RMS error of ∼21.0% compared with an equivalent Long Short-Term Memory (LSTM) architecture. Our framework was validated in simulation, demonstrating the ability to generalize to materials previously unseen in the training dataset.

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“…Therefore, using learning-based model predictive control, we aim to learn a discrete-time state-space representation of the task dynamics as

Here

) taken at each time step into the learned model.…”

Section: Methodsmentioning

confidence: 99%

Section: Control System Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Vision-Guided MPC for Robotic Path Following Using Learned Memory-Augmented Model

Rastegarpanah

Hathaway

2021

Front. Robot. AI

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“…The stability of the overall closedloop system composed of the robot and the i-MOGIC is proved using Lyapunov arguments. Mathew et al (2019) implemented an IL-based forward model approach with incremental adaptation capability of a statedependent, time-independent impedance parameters. Moreover, their approach includes a hybrid force-motion controller that provides compliance in particular directions while adapting the impedance in other directions.…”

Section: Vilc Via Imitation Learningmentioning

confidence: 99%

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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“…on a variable impedance controller for continuous contact tasks (Mathew et al 2019), an extension for piecewisecontinuous dynamics due to contact changes (Sidhik et al 2020), and a recent study which explored a contact-changehandling module for dealing with the discontinuities in dynamics during contact changes (Sidhik et al 2021). We advocate the need for a hybrid model with one or more dynamics modes, each modeled for a contact mode with a predictive (forward) dynamics model of sensor measurements, a control law, and a relevance condition.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Framework for Reliable Trajectory Following in Changing-Contact Robot Manipulation Tasks

Sidhik¹,

Sridharan²,

Ruiken³

2021

Preprint

Self Cite

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We describe a framework for changing-contact robot manipulation tasks that require the robot to make and break contacts with objects and surfaces. The discontinuous interaction dynamics of such tasks make it difficult to construct and use a single dynamics model or control strategy, and the highly non-linear nature of the dynamics during contact changes can be damaging to the robot and the objects. We present an adaptive control framework that enables the robot to incrementally learn to predict contact changes in a changing contact task, learn the interaction dynamics of the piece-wise continuous system, and provide smooth and accurate trajectory tracking using a task-space variable impedance controller. We experimentally compare the performance of our framework against that of representative control methods to establish that the adaptive control and incremental learning components of our framework are needed to achieve smooth control in the presence of discontinuous dynamics in changing-contact robot manipulation tasks.

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Online Learning of Feed-Forward Models for Task-Space Variable Impedance Control

Cited by 7 publications

References 26 publications

Vision-Guided MPC for Robotic Path Following Using Learned Memory-Augmented Model

Vision-Guided MPC for Robotic Path Following Using Learned Memory-Augmented Model

Variable Impedance Control and Learning—A Review

An Adaptive Framework for Reliable Trajectory Following in Changing-Contact Robot Manipulation Tasks

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