Thai Duong

I am a Ph.D. candidate in the Department of Electrical and Computer Engineering at University of California, San Diego. I work at the Existential Robotics Laboratory and am fortunate to be advised by Prof. Nikolay Atanasov.

Before moving to San Diego, I worked as a software engineer at Microsoft. I obtained my M.S. degree from Oregon State University, Corvallis, Oregon and B.S. degree from Hanoi University of Science and Technology, Hanoi, Vietnam.

Email  /  Google Scholar  /  Github

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Research Interests

I am interested in robotics, machine learning, control theory, and optimization. My work focuses on robots' understanding of the environments, e.g. probablistic mapping, navigation and exploration; and of their own dynamics model, e.g. robot dynamics learning, model-based reinforcement learning, and learning from demonstration. I am also interested in modeling uncertainty in map representations and robots' dynamics for safe and active planning and control.

Projects for robot dynamics learning and control
LEMURS: Learning Distributed Multi-Robot Interactions
Eduardo Sebastian, Thai Duong, Nikolay Atanasov, Eduardo Montijano , Carlos Sagues
Accepted to ICRA'23. 🎉🎉🎉🎉🎉 .
website / arXiv

We develop LEMURS, an algorithm for learning scalable multi-robot control policies from cooperative task demonstrations. We propose a port-Hamiltonian description of the multi-robot system to exploit universal physical constraints in interconnected systems and achieve closed-loop stability. We represent a multi-robot control policy using an architecture that combines self-attention mechanisms and neural ordinary differential equations. The former handles time-varying communication in the robot team, while the latter respects the continuous-time robot dynamics. Our representation is distributed by construction, enabling the learned control policies to be deployed in robot teams of different sizes.

Figures: Learn distributed control policy for swapping with 4 robots (top) and deploy with 64 robots (bottom)

Robust and Safe Autonomous Navigation for Systems with Learned SE(3) Hamiltonian Dynamics
Zhichao Li*, Thai Duong*, Nikolay Atanasov
Learning for Dynamics and Control (L4DC), 2022. *Equal contribution.
Journal version accepted to OJ-CSYS Special Issue: Intersection of Machine Learning with Control, 2022, as invited paper.
website / video / arXiv

This paper proposes techniques to learn the dynamics models of a mobile robot from trajectory data and synthesize a tracking controller with safety and stability guarantees. Instead of a hand-derived dynamics model, we use a dataset of state-control trajectories to train a translation-equivariant nonlinear Hamiltonian model represented as a neural ordinary differential equation (ODE) network. The learned Hamiltonian model is used to synthesize an energy-shaping passivity-based controller and derive conditions which guarantee safe regulation to a desired reference pose. Finally, we enable adaptive tracking of a desired path, subject to safety constraints obtained from obstacle distance measurements. Our safe adaptive controller is demonstrated on a simulated hexarotor robot navigating in unknown complex environments.

Adaptive Control of SE(3) Hamiltonian Dynamics with Learned Disturbance Features
Thai Duong, Nikolay Atanasov
Accepted to L-CSS, 2022.
website / video / arXiv

This paper develops geometric adaptive control with a learned disturbance model for rigid-body systems, such as ground, aerial, and underwater vehicles, that satisfy Hamilton's equations of motion over the SE(3) manifold. Our design consists of an offline disturbance model identification stage, using a Hamiltonian-based neural ordinary differential equation (ODE) network trained from state-control trajectory data, and an online adaptive control stage, estimating and compensating the disturbances based on geometric tracking errors. We demonstrate our adaptive geometric controller in trajectory tracking simulations of fully-actuated pendulum and under-actuated quadrotor systems.

Trajectory tracking + learned dynamics

Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control
Thai Duong, Nikolay Atanasov
Robotics: Science and Systems (RSS), 2021.
website / video / arXiv

The dynamics of many robots, including ground, aerial, and underwater vehicles, are described in terms of their SE(3) pose and generalized velocity, and satisfy conservation of energy principles. This paper proposes a Hamiltonian formulation over the SE(3) manifold of the structure of a neural ordinary differential equation (ODE) network to approximate the dynamics of a rigid body. In contrast to a black-box ODE network, our formulation guarantees total energy conservation by construction. We develop energy shaping and damping injection control for the learned, potentially underactuated SE(3) Hamiltonian dynamics to enable a unified approach for stabiliziation and trajectory tracking with various platforms, including pendulum, rigid-body, and quadrotor systems.

Data collection from manual flights

Projects for environment understanding
Autonomous Navigation in Unknown Environments with Sparse Bayesian Kernel-based Occupancy Mapping
Thai Duong, Michael Yip, Nikolay Atanasov
Accepted to IEEE Transactions on Robotics (T-RO), 2022
website / video / arXiv

This paper focuses on online occupancy mapping and real-time collision checking onboard an autonomous robot navigating in a large unknown environment. We develop a probabilistic formulation based on Relevance Vector Machines, allowing probabilistic occupancy classification and supporting autonomous navigation. We provide an online training algorithm, updating the sparse Bayesian map incrementally from streaming range data, and an efficient collision-checking method for general curves, representing potential robot trajectories.

Autonomous Navigation in Unknown Environments using Sparse Kernel-based Occupancy Mapping
Thai Duong, Nikhil Das, Michael Yip, Nikolay Atanasov
International Conference on Robotics and Automation (ICRA), 2020
website / video / arXiv

We propose a new map representation, in which occupied and free space are separated by the decision boundary of a kernel perceptron classifier. We develop an online training algorithm that maintains a very sparse set of support vectors to represent obstacle boundaries in configuration space. We also derive conditions that allow complete (without sampling) collision-checking for piecewise-linear and piecewise-polynomial robot trajectories.

Projects for robot safety
Safe Robot Navigation in Cluttered Environments using Invariant Ellipsoids and a Reference Governor
Zhichao Li, Thai Duong, Nikolay Atanasov
ArXiv, 2020

We consider a control-affine nonlinear robot system subject to bounded input noise and rely on feedback linearization to determine ellipsoid output bounds on the closed-loop robot trajectory under stabilizing control. A virtual governor system is developed to adaptively track a desired navigation path, while relying on the robot trajectory bounds to slow down if safety is endangered and speed up otherwise. The main contribution is the derivation of theoretical guarantees for safe nonlinear system path-following control and its application to autonomous robot navigation in unknown environments.

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