Uploaded July 2021 | Updated September 2026, 1 day ago
Embodied AI Lecture Series @ PRIOR
Visit prior.allenai.org/lectures for the full list of our lectures
Title: Brain-Body Co-Optimization of Embodied Machines
Nick Cheney • University of Vermont • (7/9/21)
Abstract:
Embodied Cognition posits that the body of an agent is not only a vessel to contain the mind, but meaningfully influences the agent's brain and contributes to its intelligent behavior through morphological computation. In this talk, I'll introduce a system for studying the role of complex brains and bodies in soft robotics, demonstrate how this system may exhibit morphological computation, and describe a particular challenge that occurs when attempting to employ machine learning to optimize embodied machines and their behavior. I'll argue that simply considering and accounting for the co-dependencies suggested by embodied cognition can help us to overcome this challenge, and suggest that this approach may be helpful to the optimization of structure and function in machine learning domains outside of soft robotics.
Embodied AI Lecture Series @ PRIOR
Visit prior.allenai.org/lectures for the full list of our lectures
Title: Brain-Body Co-Optimization of Embodied Machines
Nick Cheney • University of Vermont • (7/9/21)
Abstract:
Embodied Cognition posits that the body of an agent is not only a vessel to contain the mind, but meaningfully influences the agent's brain and contributes to its intelligent behavior through morphological computation. In this talk, I'll introduce a system for studying the role of complex brains and bodies in soft robotics, demonstrate how this system may exhibit morphological computation, and describe a particular challenge that occurs when attempting to employ machine learning to optimize embodied machines and their behavior. I'll argue that simply considering and accounting for the co-dependencies suggested by embodied cognition can help us to overcome this challenge, and suggest that this approach may be helpful to the optimization of structure and function in machine learning domains outside of soft robotics.
