Learning Non-linear Multivariate Dynamics of Motion in Robotic Manipulators

Gribovskaya, Elena; Khansari-Zadeh, Seyed Mohammad; Billard, Aude

doi:10.1177/0278364910376251

Cited by 100 publications

(96 citation statements)

References 84 publications

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“…In previous work of ours, we addressed different aspects of encoding motions with dynamical systems, specifically: effective and smooth adaptation in the case of spatio-temporal perturbations [14], learning of asymptotically stable estimates [19], learning of position and orientation control for motion generation [1]. The present paper continues research in this direction and presents new results on learning motions of both the robot's end-effector and external objects.…”

Section: Introductionmentioning

confidence: 83%

“…Next, we briefly review these works according to (1) how they predict trajectories of moving objects and (2) how they generate the robot's motions [14]. To catch a moving object properly, prediction of the trajectory of moving objects is required.…”

Section: Introductionmentioning

confidence: 99%

“…This problem has been largely overlooked in the literature sofar. In [14], we proposed a means to learn timeindependent dynamical systems (autonomous dynamical systems) so as to become robust to temporal perturbations. Here, we extend this work and address the problem of controlling the duration of the motion when encoded in an autonomous DS.…”

Section: Introductionmentioning

confidence: 99%

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Learning motion dynamics to catch a moving object

Kim

Gribovskaya

Billard

2010

2010 10th IEEE-RAS International Conference on Humanoid Robots

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Abstract-In this paper, we consider a novel approach to control the timing of motions when these are encoded with autonomous dynamical systems(DS). Accurate timing of motion is crucial if a robot must synchronize its movement with that of a fast moving object. In previous work of ours [1], we developed an approach to encode robot motion into DS. Such a timeindependent encoding is advantageous in that it offers robustness against violent perturbation by adapting on the fly the trajectory while ensuring high accuracy at the target. We propose here an extension of the system that allows to control the timing of the motion while still benefitting from all the robustness properties deriving from the time-independent encoding of the DS. We validate the approach in experiments where the iCub robot learns from human demonstrations to catch a ball on the fly.

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Section: Introductionmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Learning motion dynamics to catch a moving object

Kim

Gribovskaya

Billard

2010

2010 10th IEEE-RAS International Conference on Humanoid Robots

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“…However, this is not applicable to tasks where the topological features of the trajectory must be adapted according to the state of the environment. Another important previous work is presented by Billard et al [7,6,4]. They developed a scheme of learning tasks as a time-invariant dynamic system and adapting the motion trajectory in realtime for obstacle avoidance [6].…”

Section: Related Studiesmentioning

confidence: 99%

Online Trajectory Planning in Dynamic Environments for Surgical Task Automation

Osa

Sugita

Mitsuishi

2014

Robotics: Science and Systems X

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Abstract-Automation of robotic surgery has the potential to improve the performance of surgeons and the quality of the life of patients. However, the automation of surgical tasks has challenging problems that must be resolved. One such problem is adaptive online trajectory planning based on the state of the surrounding dynamic environment. This study presents a framework for online trajectory planning in a dynamic environment for automatic assistance in robotic surgery. In the proposed system, a demonstration under various states of the environment is used for learning. The distribution of the demonstrated trajectory over the environmental conditions is modeled using a statistical model. The trajectory, under given environmental conditions, is computed as a conditional expectation using the learned model. Because of its low computational cost, the proposed scheme is able to generalize and plan a trajectory online in a dynamic environment. To design the motion of the system to track the planned trajectory in a stable and smooth manner, the concept of a sliding mode control was employed; its stability was proved theoretically. The proposed scheme was implemented on a robotic surgical system and the performance was verified through experiments and simulations. These experiments and simulations verified that the developed system successfully planned and updated the trajectories of the learned tasks in response to the changes in the dynamic environment.

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“…It is then sufficient to spread the basis function regularly along this single dimension. The second approach, which is less common, is to use the state variables of the system as input to the basis function [12,7,13]. Those two approaches have different limitations.…”

Section: Introductionmentioning

confidence: 99%

Learning motions from demonstrations and rewards with time-invariant dynamical systems based policies

Rey

Kronander

Farshidian³

et al. 2017

Auton Robot

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An important challenge when using Reinforcement Learning for learning motions in robotics is the choice of parameterization for the policy. We use Gaussian Mixture Regression to extract a parameterization with relevant non-linear features from a set of demonstrations of a motion following the paradigm of Learning from Demonstration. The resulting parameterization takes the form of a non-linear time-invariant dynamical system (DS). We use this time-invariant DS as a parameterized policy for a variant of the PI 2 policy search algorithm. This paper contributes by adapting PI 2 for our time-invariant motion representation. We introduce two novel parameter exploration schemes that can be used to 1) sample model parameters to achieve a uniform exploration in state space and 2) explore while ensuring stability of the resulting motion model. Additionally, a state dependent stiffness profile is learned simultaneously to the reference trajectory and both are used together in a variable impedance control architecture. This learning architecture is validated in a hardware experiment consisting of a digging task using a KUKA LWR platform.

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Learning Non-linear Multivariate Dynamics of Motion in Robotic Manipulators

Cited by 100 publications

References 84 publications

Learning motion dynamics to catch a moving object

Learning motion dynamics to catch a moving object

Online Trajectory Planning in Dynamic Environments for Surgical Task Automation

Learning motions from demonstrations and rewards with time-invariant dynamical systems based policies

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