Hierarchical MOSAIC for movement generation

Haruno, Masahiko; Wolpert, Daniel M.; Kawato, Mitsuo

doi:10.1016/s0531-5131(03)00190-0

Cited by 111 publications

(107 citation statements)

References 22 publications

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“…In this case, different modules carry different motor solutions. Furthermore, it has been suggested that MOSAIC could be organized in a hierarchical fashion (Haruno, Wolpert, & Kawato, 2003), thus leading towards a model with chaining. Given sufficient levels, this could even provide a means for understanding other people's actions (Wolpert, Doya, & Kawato, 2003).…”

Section: Computational Modelsmentioning

confidence: 99%

Evidence for a distributed hierarchy of action representation in the brain

Grafton

Hamilton

2007

Human Movement Science

413

350

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Complex human behavior is organized around temporally distal outcomes. Behavioral studies based on tasks such as normal prehension, multi-step object use and imitation establish the existence of relative hierarchies of motor control. The retrieval errors in apraxia also support the notion of a hierarchical model for representing action in the brain. In this review, three functional brain imaging studies of action observation using the method of repetition suppression are used to identify a putative neural architecture that supports action understanding at the level of kinematics, object centered goals and ultimately, motor outcomes. These results, based on observation, may match a similar functional anatomic hierarchy for action planning and execution. If this is true, then the findings support a functional anatomic model that is distributed across a set of interconnected brain areas that are differentially recruited for different aspects of goal oriented behavior, rather than a homogeneous mirror neuron system for organizing and understanding all behavior.

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Section: Computational Modelsmentioning

confidence: 99%

Evidence for a distributed hierarchy of action representation in the brain

Grafton

Hamilton

2007

Human Movement Science

413

350

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“…The first method, PSLE-Comparison, takes inspiration from the MOSAIC architecture [2], [24] and computes the responsibility signal λ β based on prediction error. The second method, PSLH-Comparison, is based on the minimum error probability between activation distributions over model H. PSLH-Comparison was designed to not only compute λ β as a function of skill match (inverse prediction error) but also include a penalty for aspects of the skill not present in the demonstration.…”

Section: Discussionmentioning

confidence: 99%

“…The first method, PSLE-Comparison, is inspired by the HMOSAIC architecture [2] and computes the responsibility signal λ β as an inverse function of the normalized prediction error, produced by PSL. The second method, PSLHComparison, is more closely built on the PSL algorithm.…”

Section: Methods For Behavior Recognitionmentioning

confidence: 99%

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Behavior recognition for Learning from Demonstration

Billing

Hellström

Janlert

2010

2010 IEEE International Conference on Robotics and Automation

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Abstract-Two methods for behavior recognition are presented and evaluated. Both methods are based on the dynamic temporal difference algorithm Predictive Sequence Learning (PSL) which has previously been proposed as a learning algorithm for robot control. One strength of the proposed recognition methods is that the model PSL builds to recognize behaviors is identical to that used for control, implying that the controller (inverse model) and the recognition algorithm (forward model) can be implemented as two aspects of the same model. The two proposed methods, PSLE-Comparison and PSLH-Comparison, are evaluated in a Learning from Demonstration setting, where each algorithm should recognize a known skill in a demonstration performed via teleoperation. PSLHComparison produced the smallest recognition error. The results indicate that PSLH-Comparison could be a suitable algorithm for integration in a hierarchical control system consistent with recent models of human perception and motor control.

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“…One class of computational models of mirror neurons focuses on the action imitation property, for instance, the modular action approaches by Demiris [4,3] and Wolpert [18,9]. These models emphasize how to generate motions by decentralized automatic modules of the action parts using mirror neurons.…”

Section: Introductionmentioning

confidence: 99%

Robot Trajectory Prediction and Recognition Based on a Computational Mirror Neurons Model

Zhong

Weber

Wermter

2011

Lecture Notes in Computer Science

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Abstract. Mirror neurons are premotor neurons that are considered to play a role in goal-directed actions, action understanding and even social cognition. As one of the promising research areas in psychology, cognitive neuroscience and cognitive physiology, understanding mirror neurons in a social cognition context, whether with neural or computational models, is still an open issue [5]. In this paper, we mainly focus on the action understanding aspect of mirror neurons, which can be regarded as a fundamental function of social cooperation and social cognition. Our proposed initial architecture is to learn a simulation of the walking pattern of a humanoid robot and to predict where the robot is heading on the basis of its previous walking trajectory.

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Hierarchical MOSAIC for movement generation

Cited by 111 publications

References 22 publications

Evidence for a distributed hierarchy of action representation in the brain

Evidence for a distributed hierarchy of action representation in the brain

Behavior recognition for Learning from Demonstration

Robot Trajectory Prediction and Recognition Based on a Computational Mirror Neurons Model

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