Source-linked AI summary
Self-organized adaptation of a simple neural circuit enables complex robot behaviour
Silke Steingrube, Marc Timme, Florentin Woergoetter, Poramate Manoonpong
TL;DR
Complex robots need fast, adaptive control to coordinate many sensory inputs and motor outputs across diverse behaviours, but existing systems have limited autonomy and behavioural repertoires. The paper uses a simple chaotic neural CPG whose controlled periodic orbits generate behaviours and whose synapses can learn sensor-motor mappings. The resulting system supports autonomous, reconfigurable reactive control with long-term synaptic storage, while remaining focused on reactive motor behaviour and kinematic walking.
Problem
Controlling many sensory inputs and motor outputs across a broad behavioural spectrum remains difficult, while existing robotic systems have limited autonomy and few behavioural patterns.
Method
The paper uses a simple intrinsically chaotic CPG that selects and stabilizes unstable periodic orbits, with synaptic plasticity for learning sensor-motor mappings.
Results
The system enables autonomous, self-organized, reconfigurable control by adaptively selecting unstable periodic orbits and supporting synaptic learning.
Takeaways & Limitations
A chaotic ground state in a simple neuron module can support versatile control of complex robots and give chaos an active role in guiding autonomous behaviour.
Takeaways & Limitations
The study focuses on reactive motor behaviour and kinematic, position-controlled walking machines; transfer to dynamic walking remains a future possibility.
Abstract
from arXiv · showhide
Controlling sensori-motor systems in higher animals or complex robots is a challenging combinatorial problem, because many sensory signals need to be simultaneously coordinated into a broad behavioural spectrum. To rapidly interact with the environment, this control needs to be fast and adaptive. Current robotic solutions operate with limited autonomy and are mostly restricted to few behavioural patterns. Here we introduce chaos control as a new strategy to generate complex behaviour of an autonomous robot. In the presented system, 18 sensors drive 18 motors via a simple neural control circuit, thereby generating 11 basic behavioural patterns (e.g., orienting, taxis, self-protection, various gaits) and their combinations. The control signal quickly and reversibly adapts to new situations and additionally enables learning and synaptic long-term storage of behaviourally useful motor responses. Thus, such neural control provides a powerful yet simple way to self-organize versatile behaviours in autonomous agents with many degrees of freedom.
1 Bernstein Center for Computational Neuroscience, 37073 Göttingen, Germany
The paper lists an affiliation with the Bernstein Center for Computational Neuroscience in Göttingen, Germany.
- The affiliation is the Bernstein Center for Computational Neuroscience in Göttingen, Germany.
- The listed location is Göttingen, Germany.
3 Network Dynamics Group, Max Planck Institute for Dynamics & Self-Organization, 37073 Göttingen, Germany
A simple chaotic CPG control circuit coordinates many sensors and motors into diverse, adaptive behaviours. Stabilized periodic orbits generate gait patterns, while chaotic dynamics support rapid behavioural switching, self-untrapping, and learned sensor-motor responses.
- Behavioural repertoire: 18 motors are coordinated into diverse behaviours, including orienting, phototaxis, obstacle avoidance, multiple gaits, and chaotic self-untrapping.Sensor-driven postprocessing combines gait outputs with environmental signals to produce targeted behaviours and behavioural combinations.
- Control architecture: The system uses a chaotic CPG to generate many periodic output patterns that serve as distinct gaits.Without control, the CPG is chaotic; control switches it reliably among periodic outputs across a wide range of adaptation rates.
- Adaptation: The robot rapidly alters behaviour in response to changing stimuli, selecting sensor-specific responses such as obstacle avoidance and fast escape gaits.The control adapts autonomously because current sensory states determine which control period is selected.
- Learning: Synaptic learning selectively strengthens relevant sensor connections while leaving uncorrelated synapses unaffected.For slope learning, the error falls to zero and the stabilized synaptic values are stored, allowing the inclination sensor to trigger the same slow-wave gait later.
- Integrated mechanism: A single CPG combines fast adaptivity and long-term synaptic plasticity through the same network components.The approach makes pattern generation robust and learning simple while supporting reconfigurable sensor-motor mappings and behavioural conjunctions.
- Scope and implications: The study demonstrates a constructive role for chaos in controlling complex robots, but evaluates only reactive motor behaviour and focuses on kinematic walking machines.The authors identify dynamic walking and longer-term motor sequences as future extensions.