8,876 research outputs found

    The role of cognitive abilities in laparoscopic simulator training

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    Learning minimally invasive surgery (MIS) differs substantially from learning open surgery and trainees differ in their ability to learn MIS. Previous studies mainly focused on the role of visuo-spatial ability (VSA) on the learning curve for MIS. In the current study, the relationship between spatial memory, perceptual speed, and general reasoning ability, in addition to VSA, and performance on a MIS simulator is examined. Fifty-three laparoscopic novices were tested for cognitive aptitude. Laparoscopic performance was assessed with the LapSim simulator (Surgical Science Ltd., Gothenburg, Sweden). Participants trained multiple sessions on the simulator until proficiency was reached. Participants showed significant improvement on the time to complete the task and efficiency of movement. Performance was related to different cognitive abilities, depending on the performance measure and type of cognitive ability. No relationship between cognitive aptitude and duration of training or steepness of the learning curve was found. Cognitive aptitude mediates certain aspects of performance during training on a laparoscopic simulator. Based on the current study, we conclude that cognitive aptitude tests cannot be used for resident selection but are potentially useful for developing individualized training programs. More research will be performed to examine how cognitive aptitude testing can be used to design training programs

    Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning

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    Intrinsically motivated spontaneous exploration is a key enabler of autonomous lifelong learning in human children. It enables the discovery and acquisition of large repertoires of skills through self-generation, self-selection, self-ordering and self-experimentation of learning goals. We present an algorithmic approach called Intrinsically Motivated Goal Exploration Processes (IMGEP) to enable similar properties of autonomous or self-supervised learning in machines. The IMGEP algorithmic architecture relies on several principles: 1) self-generation of goals, generalized as fitness functions; 2) selection of goals based on intrinsic rewards; 3) exploration with incremental goal-parameterized policy search and exploitation of the gathered data with a batch learning algorithm; 4) systematic reuse of information acquired when targeting a goal for improving towards other goals. We present a particularly efficient form of IMGEP, called Modular Population-Based IMGEP, that uses a population-based policy and an object-centered modularity in goals and mutations. We provide several implementations of this architecture and demonstrate their ability to automatically generate a learning curriculum within several experimental setups including a real humanoid robot that can explore multiple spaces of goals with several hundred continuous dimensions. While no particular target goal is provided to the system, this curriculum allows the discovery of skills that act as stepping stone for learning more complex skills, e.g. nested tool use. We show that learning diverse spaces of goals with intrinsic motivations is more efficient for learning complex skills than only trying to directly learn these complex skills

    Simulating development in a real robot: on the concurrent increase of sensory, motor, and neural complexity

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    We present a quantitative investigation on the effects of a discrete developmental progression on the acquisition of a foveation behavior by a robotic hand-arm-eyes system. Development is simulated by (a) increasing the resolution of visual and tactile systems, (b) freezing and freeing mechanical degrees of freedom, and (c) adding neuronal units to the neural control architecture. Our experimental results show that a system starting with a low-resolution sensory system, a low precision motor system, and a low complexity neural structure, learns faster that a system which is more complex at the beginning
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