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How Do Humans Learn to Control Unstable Objects? Studies of Model-Based Planning and State-Dependant Force Control

How Do Humans Learn to Control Unstable Objects? Studies of Model-Based Planning and State-Dependant Force Control
人类如何学习控制不稳定的物体?
批准号:
9900684
负责人:
Ferdinando Mussa-Ivaldi
金额:
$26.12万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
1999
资助国家:
美国
项目状态:
已结题
起止时间:
1999-08-01 至 2003-04-30

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中文摘要
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英文摘要
9900684Mussa-IvaldiWhile significant progress has been made in our understanding of the mechanisms for executing coordinated arm movements, little is known yet about the sensory-motor processes involved in the skillful manipulation of objects. Common activities such as handling silverware and brushing teeth involve the control of potentially unstable objects. The term 'instability' refers to situations in which small deviations in one's actions can have radical consequences. For example, a small tilt of the hand can spill the contents of a spoon. The proposed research will study human learning and performance of experimental manipulation tasks based on simple unstable objects implemented by means of robotic simulation.Our investigations are aimed at better understanding the interplay between feedback mechanisms and preprogrammed actions in dealing with unstable objects. The work is organized around three specific aims. First, the proposed experiments will test whether internal models are formed during object manipulation, while maintaining the equilibrium of unstable objects. Earlier studies of arm control suggest that the central nervous system makes use of internal models of limb dynamics in order to derive the neuromuscular commands needed to carry out successful reaching movements, but it is not known whether similar strategies are used for object manipulation. Second, when handling an object we receive both visual information and information conveyed by the mechanical interaction between the hand and the object. This information is essential to generate the appropriate corrective actions but it may not be used in the same way as learning progresses. The proposed experiments will test the hypothesis that subjects become less reliant on visual monitoring of the object and more reliant on automatic corrections produced by muscle and reflex mechanics after attaining proficiency in a manipulation task. In a final set of experiments we will seek to determine effective training strategies for learning to handle unstable objects.We expect that this work will advance our understanding of how healthy people handle unstable objects. The achievement of this goal will provide a richer knowledge base for rehabilitation of motor functions lost to stroke and other neurological diseases.
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Integrating Human and Machine Learning for Enabling Co-Adaptive Body-Machine Interfaces
  • 批准号:
    2054406
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    $71.42万
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    2021
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    1632259
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    2016
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2015 International Workshop on Robotics and Interactive Technologies For Neuroscience and Rehabilitation
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    1542307
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    Standard Grant
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    $4.45万
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    2015
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    $22.65万
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    2002
  • 负责人:
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