# Research

**Theory of Human Augmentation using Variable Stiffness Technology**

The goal of this research is to enable human performance augmentation – **without external energy**. In this lab we develop a special type of spring that can change stiffness, i.e a **variable stiffness spring**, to enable human limbs to **supply more energy and exert greater forces **to complete a task, for example running faster or jumping higher. For example, using this similar functionality**, one can effectively run 50% faster without using batteries:**

A. Sutrisno and **D. J. Braun**, How to Run 50% Faster without External Energy, *Science Advances*, 2020.

**Novel Compliant Actuators for Robots & Humans**

The goal of this research is to develop an actuator that is able to exert force to complete required tasks – using as little energy as possible. This is done by designing an actuator that consists of a motor attached to compliant elements capable of storing energy. Using compliant elements to exert force minimizes the force the motor must exerts, and therefore the energy supply the actuator consumes. Also, using compliant elements allows one to recycle energy when redirecting velocity, instead of dissipating velocity before supplying energy to accelerate.

**D.J. Braun**, V. Chalvet and A. Dahiya, **Positive-Negative Stiffness Actuators**, *IEEE Transactions on Robotics*, 2018.

**Data-driven Optimal Control of Robots**

Optimal control provides a systematic approach to control robots. However, optimal controllers computed using inexact model information lead to significant performance degradation. One way to avoid this limitation is to completely discard model information. However, model-free optimal control approaches are computationally expensive (even inexact models can significantly speed up the optimal control computation).

Y. Chen and **D.J. Braun**, Hardware-in-the-Loop Iterative Optimal Feedback Control without Model-based Future Prediction, *IEEE Transactions on Robotics*, 2019.

**Reverse-Engineering Nature for Locomotion Control in Robots and Humans**

The goal of this research is to discover the *physical reasons* and control principles that govern movements of humans and animals and to utilize these findings to *build next generation robotic devices*. We have focused on the control aspect of this problem by (1) developing an *impedance controller* that is capable of generating *autonomous self-organized locomotion, *(2) extracting controllers form experimental data consistent with human locomotion, (3) developing physics base simulation tools to predict the motion of robot locomotion, (4) developing optimization based constrained impedance controllers that simultaneously provide cyclic limb coordination and upper body stabilization during the inherently unstable task of bipedal locomotion.

**D.J. Braun**, Jason E. Mitchell and M. Goldfarb, Actuated Dynamic Walking in a Seven-Link Biped Robot, *IEEE/ASME Transactions on Mechatronics*, vol. 17, no.1, pp. 147-156, 2012.

**Numerical Modeling, Simulation and Control of Robots**

We conduct physics-based simulation and optimization-based control of walking robots treated as constrained dynamical systems that interact with their environment. Simulation of constrained dynamical systems, modeled with *differential algebraic equations (DAEs),* are required in many engineering disciplines. However unlike ordinary differential equations (ODEs), that can be effectively integrated with explicit numerical methods (e.g., Runge-Kutta),* integration of DAEs often requires sophisticated implicit integrators enhanced with iterative constraint stabilization.*

Y. Li, H. Yu, and **D.J. Braun**, **Algorithmic Resolution of Multiple Impacts in Non-smooth Mechanical Systems with Switching Constraints**, IEEE International Conference on Robotics and Automation, 2019.