Skill acquisition from human demonstration using a hidden Markov model

Hovland, Geir; Sikka, Pavan; McCarragher, B.J.

doi:10.1109/robot.1996.506571

Cited by 111 publications

(53 citation statements)

References 7 publications

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“…Furthermore, Calinon [7] used different types of parametrized regressions to adjust the trajectory learnt during the demonstrations. Similarly to the GMM, a hidden Markov model (HMM) [8] can be used to represent a trajectory. Also the HMM can be parametrized [9].…”

Section: A Lbd Related Workmentioning

confidence: 99%

An Intervention-AUV learns how to perform an underwater valve turning

Carrera

Palomeras

Ribas

et al. 2014

Oceans 2014 - Taipei

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Abstract-Intervention autonomous underwater vehicles (IAUVs) are a promising platform to perform intervention task in underwater environments, replacing current methods like remotely operate underwater vehicles (ROVs) and manned submersibles that are more expensive. This article proposes a complete system including all the necessary elements to perform a valve turning task using an I-AUV. The knowledge of an operator to perform the task is transmitted to an I-AUV by a learning by demonstration (LbD) algorithm. The algorithm learns the trajectory of the vehicle and the end-effector to accomplish the valve turning. The method has shown its feasibility in a controlled environment repeating the learned task with different valves and configurations.

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Section: A Lbd Related Workmentioning

confidence: 99%

An Intervention-AUV learns how to perform an underwater valve turning

Carrera

Palomeras

Ribas

et al. 2014

Oceans 2014 - Taipei

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show abstract

“…The LfD literature may be divided into two categories: those which learn plans [22,31] and those which learn (usually stateless) policies [3,19] (for stateful examples see [8,13]). In most cases, the plan literature builds sparse machines describing occasional changes in behavior, whereas many, but not all, policy methods learn fine-grained changes in action, such as might be found in trajectory planning or control.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Training of Team Soccer Behaviors

Sullivan

Luke

2013

RoboCup 2012: Robot Soccer World Cup XVI

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Abstract. Training robot or agent behaviors by example is an attractive alternative to directly coding them. However training complex behaviors can be challenging, particularly when it involves interactive behaviors involving multiple agents. We present a novel hierarchical learning from demonstration system which can be used to train both single-agent and scalable cooperative multiagent behaviors. The methodology applies manual task decomposition to break the complex training problem into simpler parts, then solves the problem by iteratively training each part. We discuss our application of this method to multiagent problems in the humanoid RoboCup competition, and apply the technique to the keepaway soccer problem in the RoboCup Soccer Simulator.

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“…[38]. The Hidden Markov model (HMM) is used for recognition and regeneration of human motion across various demonstrations [39,40], and to teach a robot to perform assembly tasks [41]. Locally Weighted Projection Regression (LWPR) is used to approximate the dynamic model of a robot arm for computed torque control [42,32], for teaching a robot to perform basic soccer skills [43], and is also applied for real-time motion learning for a humanoid robot [32].…”

Section: Machine Learning Techniques For Modeling Robotic Tasksmentioning

confidence: 99%

Estimating the non-linear dynamics of free-flying objects

Kim

Billard

2012

Robotics and Autonomous Systems

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a b s t r a c tThis paper develops a model-free method to estimate the dynamics of free-flying objects. We take a realistic perspective to the problem and investigate tracking accurately and very rapidly the trajectory and orientation of an object so as to catch it in flight. We consider the dynamics of complex objects where the grasping point is not located at the center of mass.To achieve this, a density estimate of the translational and rotational velocity is built based on the trajectories of various examples. We contrast the performance of six non-linear regression methods (Support Vector Regression (SVR) with Radial Basis Function (RBF) kernel, SVR with polynomial kernel, Gaussian Mixture Regression (GMR), Echo State Network (ESN), Genetic Programming (GP) and Locally Weighted Projection Regression (LWPR)) in terms of precision of recall, computational cost and sensitivity to choice of hyper-parameters. We validate the approach for real-time motion tracking of 5 daily life objects with complex dynamics (a ball, a fully-filled bottle, a half-filled bottle, a hammer and a pingpong racket). To enable real-time tracking, the estimated model of the object's dynamics is coupled with an Extended Kalman Filter for robustness against noisy sensing.

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Skill acquisition from human demonstration using a hidden Markov model

Cited by 111 publications

References 7 publications

An Intervention-AUV learns how to perform an underwater valve turning

An Intervention-AUV learns how to perform an underwater valve turning

Real-Time Training of Team Soccer Behaviors

Estimating the non-linear dynamics of free-flying objects

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