Combining Optimal and Neuromuscular Controllers for Agile and Robust Humanoid Behavior

Overview. A central conundrum in robotics is that manually designed controllers are often much more robust than model-based controllers, but model-based controllers are easier to command and coordinate with perceptual systems such as vision. The overall goal of this proposal is to test the hypothesis that a hybrid controller, combining a manually designed neuromuscular model of human control and an optimizationbased controller, can achieve the desirable features of both types of control. Exploring hybrid control could lead to agile and robust robot legged locomotion, give us better understanding of the science of locomotion, and potentially lead to insights into human locomotion. Performance goals include human-like walking, achieving human speeds, step length, and cadence; control of locomotion including starting, stopping, speed, and steering; walking on rough terrain including slopes, slippery surfaces, and granular material such as dirt and soil; and whole body locomotion, including the use of hand rails, grab bars, canes, and walkers. The researchers have several legged robots (Sarcos Primus and Atrias), and access to others (Atlas and Coman) for comparative evaluation of current approaches and proposed hybrid approaches. They will evaluate the ability to match human speeds, step lengths, and cadence, control of speed and direction, rough terrain locomotion, whole body locomotion, and the ability to recover from disturbances and errors such as pushes, slipping, and tripping. They will also compare robot and human kinematics and dynamics, including joint velocies and torques, head velocity and acceleration, and ground reaction forces. The project involves theoretical research on nonlinear control, development of new control algorithms, as well as experimental research verifying control theory on humanoid robots. A successful outcome could trigger a paradigm shift in control approaches to robust behavior. Intellectual Merit. The direct contributions of this research will be: (1) A mathematical framework blending model-based and parametric policy-based control for whole body locomotion in robotics. The framework will be independent of the particular policy, although we focus on a neuromuscular policy as an example in this project. The framework may transfer to other scientific domains where blending optimization with domain knowledge is beneficial. (2) The experimental evaluation in simulation and humanoid robot testbeds of three specific approaches within this framework that focus on the policy as a cost, constraint, or replacement. (3) An understanding of how well these types of blended control generalize across robot platforms of different morphology and sensory and actuation modalities. (4) New hypotheses about human sensory-motor control of locomotion behaviors through the advancement of the neuromuscular policy. The direct contributions of this research are a deeper understanding of the benefits of combining modelbased and parametric policy-based control for locomotion performance in robotics and a comparison of walking controllers for humanoids that robustly navigate complex and uncertain environments. More broadly, the proposed research will contribute to better understanding of robust nonlinear control, and may lead to better understanding of human sensorimotor control. Broader Impacts. The research will lead to more useful robots. A successful outcome will enable practical controllers for robust locomotion of legged robots in uncertain environments with applications in disaster response and elderly care. The project may generate new insights into human sensorimotor control of behavior, which can lead to better prostheses and exoskeletons and more effective approaches to fall risk management. The proposed project integrates research into education and mentoring by directly training graduate and undergraduate students and by incorporating research outcomes into course work. The project strengthens the infrastructure for research and education by improving the robot testbeds available for research and education. The project supports the broad dissemination of research results by publishing and freely providing simulation and implementation codes. We also expect unplanned broader impacts such as appearing in the news, on TV shows, and in movies. Unplanned broader impacts from previous work include our technologies being demonstrated on entries in the DARPA Robotics Challenge, a graduate student being