Designs for robots are often inspired by animals, as they are designed mimicking animals’
mechanics, motions, behaviours and learning. The Drosophila, known as the
fruit fly, is a well-studied model animal. In this thesis, the Drosophila larva is studied
and the results are applied to robots. More specifically: a part of the Drosophila larva’s
neural circuit for operant learning is modelled, based on which a synaptic plasticity
model and a neural circuit model for operant learning, as well as a dynamic neural network
for robot reinforcement learning, are developed; then Drosophila larva’s motor
system for locomotion is studied, and based on it a soft robot system is designed.
Operant learning is a concept similar to reinforcement learning in computer science,
i.e. learning by reward or punishment for behaviour. Experiments have shown
that a wide range of animals is capable of operant learning, including animal with only
a few neurons, such as Drosophila. The fact implies that operant learning can establish
without a large number of neurons. With it as an assumption, the structure and dynamics
of synapses are investigated, and a synaptic plasticity model is proposed. The
model includes nonlinear dynamics of synapses, especially receptor trafficking which
affects synaptic strength. Tests of this model show it can enable operant learning at the
neuron level and apply to a broad range of NNs, including feedforward, recurrent and
spiking NNs.
The mushroom body is a learning centre of the insect brain known and modelled
for associative learning, but not yet for operant learning. To investigate whether it participates
in operant learning, Drosophila larvae are studied with a transgenic tool by
my collaborators. Based on the experiment and the results, a mushroom body model
capable of operant learning is modelled. The proposed neural circuit model can reproduce
the operant learning of the turning behaviour of Drosophila larvae.
Then the synaptic plasticity model is simplified for robot learning. With the simplified
model, a recurrent neural network with internal neural dynamics can learn to
control a planar bipedal robot in a benchmark reinforcement learning task which is
called bipedal walker by OpenAI. Benefiting efficiency in parameter space exploration
instead of action space exploration, it is the first known solution to the task with reinforcement
learning approaches.
Although existing pneumatic soft robots can have multiple muscles embedded in
a component, it is far less than the muscles in the Drosophila larva, which are well-organised
in a tiny space. A soft robot system is developed based on the muscle pattern
of the Drosophila larva, to explore the possibility to embed a high density of muscles
in a limited space. Three versions of the body wall with pneumatic muscles mimicking
the muscle pattern are designed. A pneumatic control system and embedded control
system are also developed for controlling the robot. With a bioinspired body wall will
a large number of muscles, the robot performs lifelike motions in experiments
