Chuong Nguyen

I am a Ph.D. candidate in the Department of Aerospace and Mechanical Engineering at University of Southern California, Los Angeles. I work at the Dynamic Robotics and Control Laboratory and am fortunate to be advised by Prof. Quan Nguyen. I obtained my M.S. degrees from University of Southern California and Gwangju Institute of Science and Technology.

I am honored to receive Viterbi Fellowship and AME Department Fellowship from USC. I am fortunate to collaborate with Prof. Nikolay Atanasov (UC San Diego), and Dr. Guillaume Bellegarda (EPFL, Switzerland)

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My research interests span optimization, control, and learning approaches for dynamic robotics including trajectory optimization, deep learning, and real-time optimization-based control.

Performing highly agile acrobatic motions with a long flight phase requires perfect timing, high accuracy, and coordination of the full-body motion. To address these challenges, we present a novel approach on timings and trajectory optimization framework for legged robots performing aggressive 3D jumping. In our method, we firstly utilize an effective optimization framework using simplified rigid body dynamics to solve for contact timings and a reference trajectory of the robot body. The solution of this module is then used to formulate a full-body trajectory optimization based on the full nonlinear dynamics of the robot. This combination allows us to effectively optimize for contact timings while ensuring that the jumping trajectory can be effectively realized in the robot hardware. We first validate the efficiency of the proposed framework on the A1 robot model for various 3D jumping tasks such as double-backflips off the high altitude of 2m. Experimental validation was then successfully conducted for various aggressive 3D jumping motions such as diagonal jumps, barrel roll, and double barrel roll from a box of heights 0.4m and 0.9m, respectively.

Performing highly agile dynamic motions, such as jumping or running on uneven stepping stones has remained a challenging problem in legged robot locomotion. This paper presents a framework that combines trajectory optimization and model predictive control to perform robust and consecutive jumping on stepping stones. In our approach, we first utilize trajectory optimization based on full-nonlinear dynamics of the robot to generate periodic jumping trajectories for various jumping distances. A jumping controller based on a model predictive control is then designed for realizing smooth jumping transitions, enabling the robot to achieve continuous jumps on stepping stones. Thanks to the incorporation of MPC as a real-time feedback controller, the proposed framework is also validated to be robust to uneven platforms with unknown height perturbations and model uncertainty on the robot dynamics.

In this paper, we consider a general task of jumping varying distances and heights for a quadrupedal robot in noisy environments, such as off of uneven terrain and with variable robot dynamics parameters. To accurately jump in such conditions, we propose a framework using deep reinforcement learning that leverages and augments the complex solution of nonlinear trajectory optimization for quadrupedal jumping. While the standalone optimization limits jumping to take-off from flat ground and requires accurate assumptions of robot dynamics, our proposed approach improves the robustness to allow jumping off of significantly uneven terrain with variable robot dynamical parameters and environmental conditions. Compared with walking and running, the realization of aggressive jumping on hardware necessitates accounting for the motors’ torque-speed relationship as well as the robot’s total power limits. By incorporating these constraints into our learning framework, we successfully deploy our policy sim-to-real without further tuning, fully exploiting the available onboard power supply and motors. We demonstrate robustness to environment noise of foot disturbances of up to 6 cm in height, or 33% of the robot’s nominal standing height, while jumping 2x the body length in distance.

The realization of highly dynamic jumping on legged robot to particular targets at high accuracy is a challenging task. This inaccurate transfer normally comes from uncertainties and unknown dynamics involved in contact dynamics and hardware model. In this paper, we are inspired by nature of practice make perfect on human and animal jumping to propose a framework based on iterative learning control to address this challenging problem. Our approach allows the robot to apply what it learns from a simple jumping task to accomplish multiple complex tasks within a few trails directly in hardware. In addition, the iterative learning can be efficiently formulated as Quadratic Programs (QP), enabling fast solving time of less than 1 second for each trail. We validate the method via extensive experiments in A1 model and hardware for various jumping tasks. From a simple short jump (e.g. forward 40cm), our learning approach enables robot to achieve multiple challenging targets such as jumping on high box of 20cm, jumping farther to 60cm, as well as jumping while carrying an unknown mass of 2kg. Our method enables the robot to reach desired position and orientation targets with the approximate errors of 1cm and 1 degree, respectively.