Podcast with Joseph Ayers on biomimetic robotics and lobster robot
How collaboration arrises and why it fails · Prof. Dr. Paul F.M.J. Verschure
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Show Notes
Can algorithmic control ever match the adaptability of a lobster navigating the ocean floor? Neuroscientist and roboticist Joseph Ayers reveals why DARPA abandoned traditional approaches and how chaos-based neural controllers are reshaping biomimetic robotics.
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In this episode, Ayers explains why conventional algorithmic robot control fails in unpredictable environments. Drawing on decades of studying lobster neurophysiology, he describes how animals use chaotic variations in their neural networks to escape situations no programmer could anticipate. The fundamental problem: you cannot pre-program escape strategies for every possible scenario an autonomous robot might encounter in the real world.
Ayers walks through four generations of robotic lobsters built since 1998, each informed by biological discoveries. The latest generation replaces state machines with true central pattern generators built from discrete-time map-based neurons developed by Nikolai Rukov. These phenomenological neuron models capture spiking, bursting, and chaotic dynamics using just two control parameters, enabling hundreds of neurons and synapses to run on a single DSP chip in real time. The coordination between six walking legs emerges from governing and governed oscillators maintaining proper phase relationships.
The conversation explores how building robots reveals gaps in biological knowledge. Ayers describes discovering that lobsters likely rely on simple bump sensing rather than sophisticated joint proprioception, and how accelerometry-based comparisons between expected and actual movement patterns can detect when the robot is stuck. He details the sensory architecture of the lobster brain, from Wiersma's classification of visual interneurons to the layered reflex systems that process optical flow, hydrodynamic flow, and obstacle contact. The discussion reveals how the robot-biology feedback loop generates new hypotheses about corollary discharge and motor control that can be tested in living animals.