Robotic Locomotion and Control

This study examines bipedal macaque skipping and hurdling mechanics, finding asymmetric leg use and CoM dynamics similar to human running.

This paper develops a kinetic model of C. elegans locomotion that reproduces various movements, including omega turns, and quantifies forces acting on body segments.

This paper presents a physiologically plausible neuromechanical model to study spinal control of human locomotion, showing that CPGs are necessary for rhythmic movement and that reflexes primarily provide inhibitory feedback.

The paper develops a hierarchical model to control insect movement via electrical stimulation, showing that stimulus-torque relationships are linear but vary between individuals.

The paper demonstrates that a spinal cord model can learn rhythmic movements using the BCM learning rule, without requiring rhythm generating neurons or genetically defined connectivity.

The paper introduces neuroWalknet, a decentralized controller for hexapod walking that exhibits context-dependent behaviors and explains biological reflexes through emergent properties.

The paper presents a neuromechanical model that demonstrates flexible walking at different gait patterns by using high-level motor planning and fixed low-level reflex loops.

This paper presents a neuromuscular model of human locomotion that integrates spinal reflex circuits with voluntary movements to generate stable, rhythmic walking patterns with real-time adaptability.

NeuroMechFly is a neuromechanical model of Drosophila melanogaster that simulates biomechanics and neural control to predict behaviors and optimize locomotion.

The paper constructs a neural network model of the stick insect's walking system to explain phase transitions in motor activity observed in isolated ganglia.

This paper presents a quadrupedal robot that achieves natural locomotion through a neurally inspired hierarchical control system without training or internal models.

The paper proposes an optimality principle for combining central pattern generators and reflex circuits in locomotion, showing how uncertainty-driven feedback optimizes gait stability and energy efficiency.

This study identifies key reflex parameters that modulate gait characteristics through neuromechanical simulations of human locomotion.

The paper investigates whether multiple network rhythmic pattern generators in C. elegans can drive forward locomotion without pacemaker neurons or stretch-receptor feedback using a simulation model.

This paper investigates the organization of locomotor CPG circuits by testing a hypothesis about state-dependent changes in rhythm generator operation using experimental and mathematical modeling approaches.

The paper questions the validity of self-inhibitory mechanisms in the half-center model of locomotion control using in vivo data and functional evaluations.

The paper investigates neuronal connectivity between central pattern generating networks in stick insect legs using experimental and modeling approaches to identify coupling structures and strengths.

The paper provides an analytical solution to a Brown-type CPG model, demonstrating its efficiency and ability to control symmetric and asymmetric locomotion through parametric sensitivity analysis.