Olalekan Ogunmolu

Olalekan Ogunmolu, Nicholas Gans, Tyler Summers

Multistage decision policies provide useful control strategies in high-dimensional state spaces, particularly in complex control tasks. However, they exhibit weak performance guarantees in the presence of disturbance, model mismatch, or model uncertainties. This brittleness limits their use in high-risk scenarios. We present how to quantify the sensitivity of such policies in order to inform of their robustness capacity. We also propose a minimax iterative dynamic game framework for designing robust policies in the presence of disturbance/uncertainties. We test the quantification hypothesis on a carefully designed deep neural network policy; we then pose a minimax iterative dynamic game (iDG) framework for improving policy robustness in the presence of adversarial disturbances. We evaluate our iDG framework on a mecanum-wheeled robot, whose goal is to find a ocally robust optimal multistage policy that achieve a given goal-reaching task. The algorithm is simple and adaptable for designing meta-learning/deep policies that are robust against disturbances, model mismatch, or model uncertainties, up to a disturbance bound. Videos of the results are on the author's website, http://ecs.utdallas.edu/~opo140030/iros18/iros2018.html, while the codes for reproducing our experiments are on github, https://github.com/lakehanne/youbot/tree/rilqg. A self-contained environment for reproducing our results is on docker, https://hub.docker.com/r/lakehanne/youbotbuntu14/

Olalekan Ogunmolu

We review Bhat et al's paper where a class of bounded, continuous time-invariant finite time stabilizing feedback laws are derived for the double integrator and Lyapunov theory is employed in establishing finite-time convergence.

Olalekan Ogunmolu, Adwait Kulkarni, Yonas Tadesse et al.

Precise patient positioning is fundamental to successful removal of malignant tumors during treatment of head and neck cancers. Errors in patient positioning have been known to damage critical organs and cause complications. To better address issues of patient positioning and motion, we introduce a 3-DOF neuro-adaptive soft-robot, called Soft-NeuroAdapt to correct deviations along 3 axes. The robot consists of inflatable air bladders that adaptively control head deviations from target while ensuring patient safety and comfort. The adaptive-neuro controller combines a state feedback component, a feedforward regulator, and a neural network that ensures correct adaptation. States are measured by a 3D vision system. We validate Soft-NeuroAdapt on a 3D printed head-and-neck dummy, and demonstrate that the controller provides adaptive actuation that compensates for intrafractional deviations in patient positioning.

Olalekan P. Ogunmolu, Xuejun Gu, Steve Jiang et al.

This work presents an on-going investigation of the control of a pneumatic soft-robot actuator addressing accurate patient positioning systems in maskless head and neck cancer radiotherapy. We employ two RGB-D sensors in a sensor fusion scheme to better estimate a patient's head pitch motion. A system identification prediction error model is used to obtain a linear time invariant state space model. We then use the model to design a linear quadratic Gaussian feedback controller to manipulate the patient head position based on sensed head pitch motion. Experiments demonstrate the success of our approach.

Olalekan Ogunmolu, Xuejun Gu, Steve Jiang et al.

Neural networks are known to be effective function approximators. Recently, deep neural networks have proven to be very effective in pattern recognition, classification tasks and human-level control to model highly nonlinear realworld systems. This paper investigates the effectiveness of deep neural networks in the modeling of dynamical systems with complex behavior. Three deep neural network structures are trained on sequential data, and we investigate the effectiveness of these networks in modeling associated characteristics of the underlying dynamical systems. We carry out similar evaluations on select publicly available system identification datasets. We demonstrate that deep neural networks are effective model estimators from input-output data