Incremental Semantically Grounded Learning from Demonstration

Niekum, Scott; Chitta, Sachin; Barto, Andrew G.; Marthi, Bhaskara; Osentoski, Sarah

doi:10.15607/rss.2013.ix.048

Cited by 75 publications

(59 citation statements)

References 17 publications

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“…The focus of our work instead is on incorporating several demonstrations with varying sequence orders into one graph model and learning the switching behavior between succeeding movements. The most similar work to ours is [8].…”

Section: B Related Workmentioning

confidence: 68%

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Learning to sequence movement primitives from demonstrations

Manschitz

Kober

Gienger

et al. 2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-We present an approach for learning sequential robot skills through kinesthetic teaching. The demonstrations are represented by a sequence graph. Finding the transitions between consecutive basic movements is treated as classification problem where both Support Vector Machines and Gaussian Mixture Models are evaluated as classifiers. We show how the observed primitive order of all demonstrations can help to improve the movement reproduction by restricting the classification outcome to the currently executed primitive and its possible successors in the graph. The approach is validated with an experiment in which a 7-DOF Barrett WAM robot learns to unscrew a light bulb.

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Section: B Related Workmentioning

confidence: 68%

“…A sequence can then be generated by sampling randomly from the graph. Finite state machines (FSMs) are akin to graphs and can also be used to model transitions between primitives [8].…”

Section: B Related Workmentioning

confidence: 99%

Learning to sequence movement primitives from demonstrations

Manschitz

Kober

Gienger

et al. 2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…This reward is discounted over time by a discount factor γ ∈ [0, 1) and the goal of the agent is to maximize the expected discounted reward at each time step. [Guenter et al 2007] object manipulation GMR [Togelius et al 2007] games/driving ANN [Schaal et al 2007] batting LWR [Calinon and Billard 2007b] object manipulation GMM, GMR [Berger et al 2008] robotics direct recording [Asfour et al 2008] object manipulation HMM [Coates et al 2008] aerial vehicle Expectation Maximization (EM) [Mayer et al 2008] robotics ANN [Kober and Peters 2009c] batting LWR [Munoz et al 2009] games/driving ANN [Cardamone et al 2009] games/driving KNN, ANN games Support Vector Machine (SVM) [Muñoz et al 2010] games/driving ANN games ANN [Geng et al 2011] robot grasping ANN [Ikemoto et al 2012] assistive robots GMM [Judah et al 2012] benchmark tasks linear logistic regression [Vlachos 2012] structured datasets online passiveaggressive algorithm [Raza et al 2012] soccer simulation ANN, NB, DT, PART [Mülling et al 2013] batting Linear Bayesian Regression [Ortega et al 2013] games ANN [Niekum et al 2013] robotics HMM robotics HMM [Vogt et al 2014] robotics ANN [Droniou et al 2014] robotics ANN [Brys et al 2015b] benchmark tasks Rule based learning [Levine et al 2015] object manipulation ANN board game ANN ACM Computing Surveys, Vol. V, No.…”

Section: Reinforcement Learningmentioning

confidence: 99%

Imitation Learning

et al. 2017

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Imitation learning techniques aim to mimic human behavior in a given task. An agent (a learning machine) is trained to perform a task from demonstrations by learning a mapping between observations and actions. The idea of teaching by imitation has been around for many years; however, the field is gaining attention recently due to advances in computing and sensing as well as rising demand for intelligent applications. The paradigm of learning by imitation is gaining popularity because it facilitates teaching complex tasks with minimal expert knowledge of the tasks. Generic imitation learning methods could potentially reduce the problem of teaching a task to that of providing demonstrations, without the need for explicit programming or designing reward functions specific to the task. Modern sensors are able to collect and transmit high volumes of data rapidly, and processors with high computational power allow fast processing that maps the sensory data to actions in a timely manner. This opens the door for many potential AI applications that require real-time perception and reaction such as humanoid robots, self-driving vehicles, human computer interaction, and computer games, to name a few. However, specialized algorithms are needed to effectively and robustly learn models as learning by imitation poses its own set of challenges. In this article, we survey imitation learning methods and present design options in different steps of the learning process. We introduce a background and motivation for the field as well as highlight challenges specific to the imitation problem. Methods for designing and evaluating imitation learning tasks are categorized and reviewed. Special attention is given to learning methods in robotics and games as these domains are the most popular in the literature and provide a wide array of problems and methodologies. We extensively discuss combining imitation learning approaches using different sources and methods, as well as incorporating other motion learning methods to enhance imitation. We also discuss the potential impact on industry, present major applications, and highlight current and future research directions.

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“…In PbD of robots, incremental learning can be divided into tasks incremental learning [15,16] and skills incremental learning. A skill describes a basic action, for example, transport and manipulation; a task is a skill sequence [16].…”

Section: Related Workmentioning

confidence: 99%

Incremental Learning of Skills in a Task-Parameterized Gaussian Mixture Model

Hoyos

Prieto

Alenyà

et al. 2015

J Intell Robot Syst

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Programming by demonstration techniques facilitate the programming of robots. Some of them allow the generalization of tasks through parameters, although they require new training when trajectories different from the ones used to estimate the model need to be added. One of the ways to re-train a robot is by incremental learning, which supplies additional information of the task and does not require teaching the whole task again. The present study proposes three techniques to add trajectories to a previously estimated task-parameterized Gaussian mixture model. The first technique estimates a new model by accumulating the new trajectory and the set of trajectories generated using the previous model. The second technique permits adding to the parameters of the existent model those obtained for the new trajectories. The third one updates the model parameters by running a modified version of the Expectation-Maximization algorithm, with the information of the new trajectories. The techniques were evaluated in a simulated task and a real one, and they showed better performance than that of the existent model.

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Incremental Semantically Grounded Learning from Demonstration

Cited by 75 publications

References 17 publications

Learning to sequence movement primitives from demonstrations

Learning to sequence movement primitives from demonstrations

Imitation Learning

Incremental Learning of Skills in a Task-Parameterized Gaussian Mixture Model

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