Learning Parametric Dynamic Movement Primitives from Multiple Demonstrations

Matsubara, Takamitsu; Hyon, Sang-Ho; Morimoto, Jun

doi:10.1007/978-3-642-17537-4_43

Cited by 29 publications

(39 citation statements)

References 9 publications

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“…Several denominations have been introduced in the literature to describe these models, such as task-parameterized [20,64,91] (the denomination used here), parametric [52,60,102], stylistic [13] or object-centric warping [57]. In these models, the encoding of skills usually serve several purposes, including classification, prediction, synthesis and online adaptation.…”

Section: Adaptive Models Of Movementsmentioning

confidence: 99%

“…In these models, the encoding of skills usually serve several purposes, including classification, prediction, synthesis and online adaptation. A taxonomy of task-parameterized models is presented in [14], with three broad categories, namely 1. approaches employing M models for the M demonstrations, performed in M different situations, see, e.g., [21,29,44,48,51,60,97], 2. approaches employing P models for the P frames of reference that are possibly relevant for the task, see, e.g., [3,25,66], and 3. approaches employing a single model whose parameters are modulated by task parameters, see, e.g., [43,52,71,72,80,102]. …”

Section: Adaptive Models Of Movementsmentioning

confidence: 99%

“…An alternative way of handling task-parameterized movements is to fit a model to each demonstration and associate it with a task-specific feature, goal, style variable or perceptual feedback, see, for example, [21,29,44,48,51,60,97]. Such approach is typically better suited for task parameters that do not change during the demonstration.…”

Section: Gaussian Process Regression (Gpr) With Trajectory Modelsmentioning

confidence: 99%

See 2 more Smart Citations

A tutorial on task-parameterized movement learning and retrieval

Calinon

2015

Intel Serv Robotics

324

348

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Task-parameterized models of movements aim at automatically adapting movements to new situations encountered by a robot. The task parameters can, for example, take the form of positions of objects in the environment or landmark points that the robot should pass through. This tutorial aims at reviewing existing approaches for task-adaptive motion encoding. It then narrows down the scope to the special case of task parameters that take the form of frames of reference, coordinate systems or basis functions, which are most commonly encountered in service robotics. Each section of the paper is accompanied by source codes designed as simple didactic examples implemented in Matlab with a full compatibility with GNU Octave, closely following the notation and equations of the article. It also presents ongoing work and further challenges that remain to be addressed, with examples provided in simulation and on a real robot (transfer of manipulation behaviors to the Baxter bimanual robot). The repository for the accompanying source codes is available at

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Section: Adaptive Models Of Movementsmentioning

confidence: 99%

Section: Adaptive Models Of Movementsmentioning

confidence: 99%

Section: Gaussian Process Regression (Gpr) With Trajectory Modelsmentioning

confidence: 99%

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A tutorial on task-parameterized movement learning and retrieval

Calinon

2015

Intel Serv Robotics

324

348

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“…Compared to representations proposed in [6,7], only a simple system of linear equations need to be solved. DMPs can be used for representing classes of movements using statistical learning techniques [2,10], for combining trajectories in a dynamic way [11,12], and for reinforcement learning [13,14,15,16]. In this paper we exploit the DMP framework to enable continuous non-rigid contact with the environment, based on force feedback.…”

Section: Introductionmentioning

confidence: 99%

Adaptation and coaching of periodic motion primitives through physical and visual interaction

Gams

Petrič

et al. 2016

Robotics and Autonomous Systems

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In this paper we propose and evaluate a control system to 1) learn and 2) adapt robot motion for continuous non-rigid contact with the environment. We present the approach in the context of wiping surfaces with robots. Our approach is based on learning by demonstration. First an initial periodic motion, covering the essence of the wiping task, is transferred from a human to a robot. The system extracts and learns one period of motion. Once the user/demonstrator is content with the motion, the robot seeks and establishes contact with a given surface, maintaining a predefined force of contact through force feedback. The shape of the surface is encoded for the complete period of motion, but the robot can adapt to a different surface, perturbations or obstacles. The novelty stems from the fact that the feedforward component is learned and encoded in a dynamic movement primitive.By using the feedforward component, the feedback component is greatly reduced if not completely canceled. Finally, if the user is not satisfied with the periodic pattern, he/she can change parts of motion through predefined gestures or through physical contact in a manner of a tutor or a coach.The complete system thus allows not only a transfer of motion, but a transfer of motion with matching correspondences, i. e. wiping motion is constrained to maintain physical contact with the surface to be wiped. The interface for both learning and adaptation is simple and intuitive and allows for fast and reliable knowledge transfer to the robot.Simulated and real world results in the application domain of wiping a surface are presented on three different robotic platforms. Results of the three robotic platforms, namely a 7 degree-of-freedom Kuka LWR-4 robot, the ARMAR-IIIa humanoid platform and the Sarcos CB-i humanoid robot, depict different methods of adaptation to the environment and coaching.

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“…DMPs provide means to encode a trajectory as a set of differential equations that can compactly represent control policies, while their attractor landscapes can be adapted by changing only a few parameters. The latter can be exploited in several ways, for example for reinforcement learning [8], [9], [10], [11], statistical generalization [12], [13], or they can even be combined in a dynamic way [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Modulation of motor primitives using force feedback: Interaction with the environment and bimanual tasks

Gams

Nemec

Žlajpah

et al. 2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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The framework of dynamic movement primitives allows the generation of discrete and periodic trajectories, which can be modulated in various aspects. We propose and evaluate a novel modulation approach that includes force feedback and thus allows physical interaction with objects and the environment. The proposed approach also enables the coupling of independently executed robotic trajectories, simplifying the execution of bimanual and tightly coupled cooperative tasks. We apply an iterative learning control algorithm to learn a coupling term, which is applied to the original trajectory in a feed-forward fashion. The coupling term modifies the trajectory in accordance to either the desired position or external force. The strengths of the approach are shown in bimanual or twoagent obstacle avoidance tasks, where no higher level cognitive reasoning or planning are required. Results of simulated and real-world experiments on the ARMAR-III humanoid robot in interaction and object lifting tasks, and on two KUKA LWR robots in a bimanual setting are presented.

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Learning Parametric Dynamic Movement Primitives from Multiple Demonstrations

Cited by 29 publications

References 9 publications

A tutorial on task-parameterized movement learning and retrieval

A tutorial on task-parameterized movement learning and retrieval

Adaptation and coaching of periodic motion primitives through physical and visual interaction

Modulation of motor primitives using force feedback: Interaction with the environment and bimanual tasks

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