Bio-Inspired Computing for Robotics

Translate a robotics capability into a measurable bio-inspired computing benchmark.

Define performance, robustness, adaptation-speed, compute, and energy metrics for a robotics experiment.

Create a reusable Python and ROS2 experiment scaffold with configurable controllers and environments.

Record configurations, random seeds, trajectories, and timing data for reproducible experiments.

Implement a conventional controller as a baseline for a bio-inspired method.

Quantify uncertainty in repeated robot experiments using confidence intervals and effect sizes.

Represent a robot controller as a genotype-to-phenotype mapping suitable for black-box optimization.

Implement an ask-tell evolution strategy for optimizing controller parameters.

Apply CMA-ES to a noisy robotics objective.

Design a behavior descriptor that separates meaningfully different robot behaviors.

Implement novelty search using a nearest-neighbor novelty metric.

Implement MAP-Elites for a two-dimensional behavior archive.

Apply an emitter-based quality-diversity method to improve archive coverage and quality.

Select robust controllers from a quality-diversity archive using perturbed evaluations.

Encode a feedforward neural controller for direct evolutionary optimization.

Implement topology and weight mutations for a NEAT-style controller.

Use compatibility distance and speciation to preserve structural innovation during neuroevolution.

Evolve a recurrent neural controller for a partially observable robotics task.

Construct a CPPN-based indirect encoding for a structured robot controller.

Formulate a multi-objective neuroevolution problem balancing task performance and resource use.

Measure the generalization of evolved policies across unseen environments.

Diagnose deceptive objectives by comparing fitness-driven and diversity-driven neuroevolution.

Simulate a nonlinear oscillator and identify its stable limit cycle.

Implement a Hopf oscillator as a rhythmic motor command generator.

Couple multiple oscillators with specified phase relationships.

Map oscillator states to joint trajectories for a simulated robot.

Tune oscillator parameters to switch between distinct locomotion patterns.

Modulate a central pattern generator using proprioceptive feedback.

Optimize central pattern generator parameters with an evolution strategy.

Evaluate gait recovery after external perturbations or simulated actuator damage.

Simulate a leaky integrate-and-fire neuron under time-varying input.

Encode continuous sensor values using rate and temporal spike codes.

Decode spike trains into continuous motor commands.

Implement spike-timing-dependent plasticity for a small spiking network.

Stabilize spiking activity with a homeostatic adaptation rule.

Train a spiking network with surrogate gradients on a sensor-classification task.

Construct a spiking policy for a closed-loop robotics task.

Compare spiking and non-spiking models by accuracy, latency, operation count, and estimated energy.

Generate controlled event-camera data in a robotics simulator.

Filter background activity and refractory noise from an event stream.

Transform an event-camera stream into time surfaces and voxel-grid representations.

Estimate image-plane motion from asynchronous events.

Train a spiking model to recognize motion patterns from simulated events.

Connect event-based perception to a reactive robot controller.

Measure end-to-end latency and bandwidth under frame-based and event-based sensing.

Partition an event-processing pipeline between software and FPGA-oriented streaming blocks.

Construct an echo-state network with stable reservoir dynamics.

Train a reservoir readout for robot state prediction.

Implement and train a sparsely wired liquid time-constant neural policy for a closed-loop robotics task.

Implement a closed-form continuous-time neural policy for irregularly sampled sensor data.

Interpret the sparse wiring of a neural circuit policy in terms of sensor-to-action pathways.

Benchmark reservoir, liquid, and spiking policies under sensor noise and timing jitter.

Combine a learned dynamical policy with a central pattern generator through bounded modulation.

Construct a local-neighborhood sensing and relative-state model for decentralized swarm control.

Produce cohesive flocking by balancing separation, alignment, and attraction rules.

Implement distributed consensus over a time-varying communication graph.

Allocate swarm tasks using a response-threshold model.

Implement stigmergic coordination through a simulated environmental signal.

Implement decentralized collision avoidance using local artificial potential fields.

Evolve a shared neural local policy for a homogeneous swarm using neuroevolution.

Measure swarm scalability and resilience under communication loss and robot failures.

Explain how compliance and passive dynamics can reduce a controller's computational burden in a specified robot design.

Model a compliant actuator or elastic joint in simulation.

Measure the contribution of passive body dynamics by comparing locked and compliant configurations.

Define morphology descriptors for a quality-diversity archive.

Co-optimize selected body parameters and controller parameters for a locomotion task.

Evaluate a body–controller design across terrain and payload variations.

Select a body–controller candidate from a co-design quality-diversity archive that maintains performance under simulated structural damage.

Formulate a discrete active-inference generative model by constructing and validating its observation, transition, preference, and prior matrices.

Compute prediction errors and belief updates in a discrete active-inference model.

Select actions by minimizing expected free energy in a small decision task.

Implement predictive-error control for a continuous robot state variable.

Add a homeostatic variable that changes a robot's action preferences.

Implement curiosity-driven exploration using prediction error or information gain.

Compare active-inference and reward-driven agents on adaptation and exploration metrics.

Instrument the reusable ROS2 experiment harness to inject controlled sensor noise and control latency.

Fit simulator parameters from system-identification data and quantify residual model mismatch.

Apply domain randomization around identified parameter uncertainty during controller optimization or training.

Impose safety and actuator constraints on the output of an adaptive controller.

Package a trained bio-inspired controller as a ROS2 component with deterministic interfaces.

Profile controller latency, memory use, and energy proxies on an embedded target or emulator.

Convert bio-inspired differential update equations to fixed-point arithmetic and quantify quantization loss.

Implement and simulate an FPGA streaming kernel for a neuromorphic or oscillator update, verifying it against a floating-point reference.

Formulate a testable capstone hypothesis for a mobile robot, UAV, manipulator, embedded platform, or swarm.

Select complementary bio-inspired components and a conventional baseline using task requirements and deployment constraints.

Specify interfaces among perception, adaptation, control, and safety components.

Integrate one perception component with one bio-inspired controller in ROS2 simulation.

Add online adaptation or quality-diversity-based controller selection to the integrated system.

Perform an ablation study that isolates the contribution of each bio-inspired component.

Stress-test the system under environmental shifts, component faults, and compute constraints.

Write a reproducible technical report that relates experimental evidence to the capstone hypothesis.