4.5.23-AI-Seethapathi

Conference Video|Duration: 25:11
April 5, 2023
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    The best current robots still fall short of the versatility, efficiency, stability, and robustness to uncertainty achieved by the human sensorimotor control system. One way to understand how such remarkable performance is achieved is to develop theoretical frameworks and computational models that capture empirical evidence of how humans select, execute, and learn movements.  In this talk, I will summarize the findings of our past and ongoing work seeking to uncover the computational principles underlying efficient locomotor control in natural environments, the strategies underlying robust locomotion in the presence of intrinsic and extrinsic perturbations, and the seamless integration of sensorimotor control with higher-level cognition across different timescales. These principles can provide a blueprint for engineers seeking to develop autonomous and robot-aided systems that exhibit intelligence comparable to biological sensorimotor control.
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  • Video details
    The best current robots still fall short of the versatility, efficiency, stability, and robustness to uncertainty achieved by the human sensorimotor control system. One way to understand how such remarkable performance is achieved is to develop theoretical frameworks and computational models that capture empirical evidence of how humans select, execute, and learn movements.  In this talk, I will summarize the findings of our past and ongoing work seeking to uncover the computational principles underlying efficient locomotor control in natural environments, the strategies underlying robust locomotion in the presence of intrinsic and extrinsic perturbations, and the seamless integration of sensorimotor control with higher-level cognition across different timescales. These principles can provide a blueprint for engineers seeking to develop autonomous and robot-aided systems that exhibit intelligence comparable to biological sensorimotor control.
Locked Interactive transcript

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