Uploaded September 2021 | Updated September 2026, 5 hours ago
Embodied AI Lecture Series @ PRIOR
For previous recordings and upcoming talks visit prior.allenai.org/lectures
Biological Robots
Josh Bongard • University of Vermont • (8/20/21)
Abstract:
Reinforcement learning has greatly accelerated our ability to train control policies for robots. But, what if the robot has no observable control policy? --and is a millimeter in diameter? --and is composed solely from biological cells? The emerging field of computer designed organisms challenges our deepest preconceptions about how to apply AI methods to embodied machines, while simultaneously offering new materials and methods for building and reasoning about the nature of planning, control, decision making, agency, and general intelligence. In this talk I will describe how our team combined evolutionary algorithms with physical simulation to “program” behavior into biobots in silico, instantiate some of the most promising designs as physical biobots, and feed back lessons learned to improve subsequent sim2real transfers. I will conclude by discussing some of the implications of this work for biologists, the artificial intelligence community, and cognitive scientists.
Embodied AI Lecture Series @ PRIOR
For previous recordings and upcoming talks visit prior.allenai.org/lectures
Biological Robots
Josh Bongard • University of Vermont • (8/20/21)
Abstract:
Reinforcement learning has greatly accelerated our ability to train control policies for robots. But, what if the robot has no observable control policy? --and is a millimeter in diameter? --and is composed solely from biological cells? The emerging field of computer designed organisms challenges our deepest preconceptions about how to apply AI methods to embodied machines, while simultaneously offering new materials and methods for building and reasoning about the nature of planning, control, decision making, agency, and general intelligence. In this talk I will describe how our team combined evolutionary algorithms with physical simulation to “program” behavior into biobots in silico, instantiate some of the most promising designs as physical biobots, and feed back lessons learned to improve subsequent sim2real transfers. I will conclude by discussing some of the implications of this work for biologists, the artificial intelligence community, and cognitive scientists.
