End-point constraints in aiming movements: effects of approach angle and speed

Breteler, Mary D. Klein; Gielen, Stan; Meulenbroek, Ruud G. J.

doi:10.1007/pl00007997

Cited by 23 publications

(14 citation statements)

References 14 publications

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“…In other words the simulator plans a grasp and reach trajectory and executes it in a simulated 3D world. The adaptive learning of motor control and trajetory planning is widely studied (for example Kawato et al, 1987;Kawato and Gomi, 1992;Jordan and Rumelhart 1992;Karniel and Inbar, 1997;Breteler et al, 2001).…”

Section: Grand Schema 2: Reach and Graspmentioning

confidence: 99%

Schema design and implementation of the grasp-related mirror neuron system

Öztop

Arbib

2002

Biological Cybernetics

264

194

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Mirror neurons within a monkey's premotor area F5 fire not only when the monkey performs a certain class of action but also when the monkey observes another monkey (or the experimenter) perform a similar action (Gallese et al. 1996;Rizzolatti et al. 1996a) . It has thus been argued that these neurons are crucial for understanding of actions by others. We offer the Hand-State Hypothesis as a new explanation of the evolution of this capability, hypothesizing that these neurons first evolved to augment the "canonical" F5 neurons (active during self-movement based on observation of an object) by providing visual feedback on "hand state", relating the shape of the hand to the shape of the object. We then introduce the MNS (Mirror Neuron System) model of F5 and related brain regions. The existing FARS (Fagg-Arbib-Rizzolatti-Sakata) model (Fagg and Arbib 1998) represents circuitry for visually-guided grasping of objects, linking parietal area AIP with F5 canonical neurons. The MNS model extends the AIP visual pathway by also modeling pathways, directed toward F5 mirror neurons, which match arm-hand trajectories to the affordances and location of a potential target object. We present the basic schemas for the MNS model, then aggregate them into three "grand schemas" − Visual Analysis of Hand State, Reach and Grasp, and the Core Mirror Circuit − for each of which we present a useful implementation. With this implementation we show how the mirror system may learn to recognize actions already in the repertoire of the F5 canonical neurons. We show that the connectivity pattern of mirror neuron circuitry can be established through training, and that the resultant network can exhibit a range of novel, physiologically interesting, behaviors during the process of action recognition. We train the system on the basis of final grasp but then observe the whole time course of mirror neuron activity, yielding predictions for neurophysiological experiments under conditions of spatial perturbation, altered kinematics, and ambiguous grasp execution which highlight the importance of the timing of mirror neuron activity.

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Section: Grand Schema 2: Reach and Graspmentioning

confidence: 99%

Schema design and implementation of the grasp-related mirror neuron system

Öztop

Arbib

2002

Biological Cybernetics

264

194

View full text Add to dashboard Cite

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“…Until now, two main cost functions have been used: minimum jerk (Flash 1987) and minimum torque change (Uno et al 1989). Some studies (Klein Breteler et al 2001;Dornay et al 1996) have shown that the minimum torque change trajectories are more similar to the observed trajectories than the trajectories simulated with the minimum jerk model.…”

Section: Introductionmentioning

confidence: 97%

“…Then, the problem of indeterminacy can be solved by defining an objective function and using dynamic optimization (Chang et al 2001;Klein Breteler et al 2001); in fact, one way to select a unique trajectory is to introduce additional constraints on the task characteristics (for example, defining the initial and final positions of the end effector), thereby further constraining the body's effective DOFs. Until now, two main cost functions have been used: minimum jerk (Flash 1987) and minimum torque change (Uno et al 1989).…”

Section: Introductionmentioning

confidence: 99%

Balance control during an arm raising movement in bipedal stance: which biomechanical factor is controlled?

et al. 2004

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In order to obtain new insight into the control of balance during arm raising movements in bipedal stance, we performed a biomechanical analysis of kinematics and dynamical aspects of arm raising movements by combining experimental work, large-scale models of the body, and techniques simulating human behavior. A comparison between experimental and simulated joint kinematics showed that the minimum torque change model yielded realistic trajectories. We then performed an analysis based on computer simulations. Since keeping the center of pressure (CoP) and the projection of the center of mass (CoM) inside the support area is essential for equilibrium, we modeled an arm raising movement where displacement of one or the other variable is limited. For this optimization model, the effects of adding equilibrium constraints on movement trajectories were investigated. The results show that: (a) the choice of the regulated variable influences the strategy adopted by the system and (b) the system was not able to regulate the CoM for very fast movements without compromising its balance. Consequently, we suggest that the system is able to maintain balance while raising the arm by only controlling the CoP. This may be done mainly by using hip mechanisms and controlling net ankle torque.

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“…Well-known costs functions usually used in robotics are: motion duration [9], minimum torque [10], global energy consumption [11], jerk for smooth motion [12], torque change [13], or any weighted combination of the above and more.…”

Section: A Optimization Problemmentioning

confidence: 99%