Learning Risk-aware Costmaps for Traversability in Challenging Environments.

One of the main challenges in autonomous robotic exploration and navigation in unknown and unstructured environments is determining where the robot can or cannot safely move. A significant source of difficulty in this determination arises from stochasticity and uncertainty, coming from localization error, sensor sparsity and noise, difficult-to-model robot-ground interactions, and disturbances to the motion of the vehicle. Classical approaches to this problem rely on geometric analysis of the surrounding terrain, which can be prone to modeling errors and can be computationally expensive.

Leveraging Stochasticity for Open Loop and Model Predictive Control of Spatio-Temporal Systems.

Stochastic spatio-temporal processes are prevalent across domains ranging from the modeling of plasma, turbulence in fluids to the wave function of quantum systems. This letter studies a measure-theoretic description of such systems by describing them as evolutionary processes on Hilbert spaces, and in doing so, derives a framework for spatio-temporal manipulation from fundamental thermodynamic principles. This approach yields a variational optimization framework for controlling stochastic fields.

Closed-loop machine-controlled CPR system optimises haemodynamics during prolonged CPR.

Objectives: We evaluated the feasibility of optimising coronary perfusion pressure (CPP) during cardiopulmonary resuscitation (CPR) with a closed-loop, machine-controlled CPR system (MC-CPR) that sends real-time haemodynamic feedback to a set of machine learning and control algorithms which determine compression/decompression characteristics over time.

Background: American Heart Association CPR guidelines (AHA-CPR) and standard mechanical devices employ a "one-size-fits-all" approach to CPR that fails to adjust compressions over time or individualise therapy, thus leading to deterioration of CPR effectiveness as duration exceeds 15-20 ​min.

Methods: CPR was administered for 30 ​min in a validated porcine model of cardiac arrest.

Sampling-Based Nonlinear Stochastic Optimal Control for Neuromechanical Systems.

Determining how the nervous system controls tendon-driven bodies remains an open question. Stochastic optimal control (SOC) has been proposed as a plausible analogy in the neuroscience community. SOC relies on solving the Hamilton-Jacobi-Bellman equation, which seeks to minimize a desired cost function for a given task with noisy controls.

Efficient Reinforcement Learning via Probabilistic Trajectory Optimization.

We present a trajectory optimization approach to reinforcement learning in continuous state and action spaces, called probabilistic differential dynamic programming (PDDP). Our method represents systems dynamics using Gaussian processes (GPs), and performs local dynamic programming iteratively around a nominal trajectory in Gaussian belief spaces. Different from model-based policy search methods, PDDP does not require a policy parameterization and learns a time-varying control policy via successive forward-backward sweeps.

A mean-field game model for homogeneous flocking.

Empirically derived continuum models of collective behavior among large populations of dynamic agents are a subject of intense study in several fields, including biology, engineering, and finance. We formulate and study a mean-field game model whose behavior mimics an empirically derived nonlocal homogeneous flocking model for agents with gradient self-propulsion dynamics. The mean-field game framework provides a non-cooperative optimal control description of the behavior of a population of agents in a distributed setting.

Cross-entropy optimization for neuromodulation.

This study presents a reinforcement learning approach for the optimization of the proportional-integral gains of the feedback controller represented in a computational model of epilepsy. The chaotic oscillator model provides a feedback control systems view of the dynamics of an epileptic brain with an internal feedback controller representative of the natural seizure suppression mechanism within the brain circuitry. Normal and pathological brain activity is simulated in this model by adjusting the feedback gain values of the internal controller.

Computational Models for Neuromuscular Function.

Computational models of the neuromuscular system hold the potential to allow us to reach a deeper understanding of neuromuscular function and clinical rehabilitation by complementing experimentation. By serving as a means to distill and explore specific hypotheses, computational models emerge from prior experimental data and motivate future experimental work. Here we review computational tools used to understand neuromuscular function including musculoskeletal modeling, machine learning, control theory, and statistical model analysis.