Incremental motion learning with locally modulated dynamical systems

Kronander, Klas; Khansari, Mohammad; Billard, Aude

doi:10.1016/j.robot.2015.03.010

Cited by 44 publications

(51 citation statements)

References 33 publications

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“…In addition, the convergence to the desired point can be ensured by DMPs. Kronander et al [2015] proposed incremental trajectory learning using a local modulation in a time-invariant dynamical system. The concept of local modulation is applicable to various vector fields.…”

Section: Incremental Trajectory Learningmentioning

confidence: 99%

An Algorithmic Perspective on Imitation Learning

et al. 2018

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As robots and other intelligent agents move from simple environments and problems to more complex, unstructured settings, manually programming their behavior has become increasingly challenging and expensive. Often, it is easier for a teacher to demonstrate a desired behavior rather than attempt to manually engineer it. This process of learning from demonstrations, and the study of algorithms to do so, is called imitation learning. This work provides an introduction to imitation learning. It covers the underlying assumptions, approaches, and how they relate; the rich set of algorithms developed to tackle the problem; and advice on effective tools and implementation.We intend this paper to serve two audiences. First, we want to familiarize machine learning experts with the challenges of imitation learning, particularly those arising in robotics, and the interesting theoretical and practical distinctions between it and more familiar frameworks like statistical supervised learning theory and reinforcement learning. Second, we want to give roboticists and experts in applied artificial intelligence a broader appreciation for the frameworks and tools available for imitation learning.We organize our work by dividing imitation learning into directly replicating desired behavior (sometimes called behavioral cloning [Bain and Sammut, 1996]) and learning the hidden objectives of the desired behavior from demonstrations (called inverse optimal control [Kalman, 1964] or inverse reinforcement learning [Russell, 1998]). In addition to method analysis, we discuss the design decisions a practitioner must make when selecting an imitation learning approach. Moreover, application examples-such as robots that play table tennis [Kober and Peters, 2009] and programs that play the game of Go [Silver et al., 2016]-illustrate the properties and motivations behind different forms of imitation learning. We conclude by presenting a set of open questions and point towards possible future research directions.

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Section: Incremental Trajectory Learningmentioning

confidence: 99%

An Algorithmic Perspective on Imitation Learning

et al. 2018

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“…LMDS guarantees local stability if the modulation is not active in a neighborhood of the equilibrium. This property is called locality in [26]. In order to ensure the locality property, we remove the last 10 points in each demonstration, creating a neighborhood of the origin without training points.…”

Section: A Accuracy Testmentioning

confidence: 99%

“…Section IV describes the related works. RDS is evaluated on a public dataset and compared with the state-of-the-art approaches [12], [26] in Section V. Section VI concludes the paper.…”

Section: Introductionmentioning

confidence: 99%

Incremental Skill Learning of Stable Dynamical Systems

Saveriano

Lee

2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Efficient skill acquisition, representation, and online adaptation to different scenarios has become of fundamental importance for assistive robotic applications. In the past decade, dynamical systems (DS) have arisen as a flexible and robust tool to represent learned skills and to generate motion trajectories. This work presents a novel approach to incrementally modify the dynamics of a generic autonomous DS when new demonstrations of a task are provided. A control input is learned from demonstrations to modify the trajectory of the system while preserving the stability properties of the reshaped DS. Learning is performed incrementally through Gaussian process regression, increasing the robot's knowledge of the skill every time a new demonstration is provided. The effectiveness of the proposed approach is demonstrated with experiments on a publicly available dataset of complex motions.

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“…In [13], a method based on Extreme Learning Machines for learning a DS that is stable in a given workspace is presented. Other approaches represent the DS in a form that makes it impossible to alter the stability properties during learning [22]. All these DS can be learned from demonstrations.…”

Section: Representing Motions For Control In Roboticsmentioning

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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Incremental motion learning with locally modulated dynamical systems

Cited by 44 publications

References 33 publications

An Algorithmic Perspective on Imitation Learning

An Algorithmic Perspective on Imitation Learning

Incremental Skill Learning of Stable Dynamical Systems

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

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