4 citations · 15 across the 20 of their papers we have counts for
20 papers
Sampling-Based Motion Planning with Scene Graphs Under Perception Constraints
Qingxi Meng, Emiliano Flores, Thai Duong +2
It will be increasingly common for robots to operate in cluttered human-centered environments such as homes, workplaces, and hospitals, where the robot is often tasked to maintain…
Ultrafast Sampling-based Kinodynamic Planning via Differential Flatness
Thai Duong, Clayton W. Ramsey, Zachary Kingston +2
Motion planning under dynamics constraints, i.e, kinodynamic planning, enables safe robot operation by generating dynamically feasible trajectories that the robot can accurately tr…
Control-Oriented System Identification: Classical, Learning, and Physics-Informed Approaches
S. Sivaranjani, Yuanyuan Shi, Nikolay Atanasov +6
We survey classical, machine learning, and data-driven system identification approaches to learn control-relevant and physics-informed models of dynamical systems. Recently, machin…
Learned IMU Bias Prediction for Invariant Visual Inertial Odometry
Abdullah Altawaitan, Jason Stanley, Sambaran Ghosal +2
Autonomous mobile robots operating in novel environments depend critically on accurate state estimation, often utilizing visual and inertial measurements. Recent work has shown tha…
Variable-Frequency Model Learning and Predictive Control for Jumping Maneuvers on Legged Robots
Chuong Nguyen, Abdullah Altawaitan, Thai Duong +2
Achieving both target accuracy and robustness in dynamic maneuvers with long flight phases, such as high or long jumps, has been a significant challenge for legged robots. To addre…
Port-Hamiltonian Neural ODE Networks on Lie Groups For Robot Dynamics Learning and Control
Thai Duong, Abdullah Altawaitan, Jason Stanley +1
Accurate models of robot dynamics are critical for safe and stable control and generalization to novel operational conditions. Hand-designed models, however, may be insufficiently…
Physics-Informed Multi-Agent Reinforcement Learning for Distributed Multi-Robot Problems
Eduardo Sebastian, Thai Duong, Nikolay Atanasov +2
The networked nature of multi-robot systems presents challenges in the context of multi-agent reinforcement learning. Centralized control policies do not scale with increasing numb…
Optimal Scene Graph Planning with Large Language Model Guidance
Zhirui Dai, Arash Asgharivaskasi, Thai Duong +4
Recent advances in metric, semantic, and topological mapping have equipped autonomous robots with semantic concept grounding capabilities to interpret natural language tasks. This…
Hamiltonian Dynamics Learning from Point Cloud Observations for Nonholonomic Mobile Robot Control
Abdullah Altawaitan, Jason Stanley, Sambaran Ghosal +2
Reliable autonomous navigation requires adapting the control policy of a mobile robot in response to dynamics changes in different operational conditions. Hand-designed dynamics mo…
Learning to Identify Graphs from Node Trajectories in Multi-Robot Networks
Eduardo Sebastian, Thai Duong, Nikolay Atanasov +2
The graph identification problem consists of discovering the interactions among nodes in a network given their state/feature trajectories. This problem is challenging because the b…
Lie Group Forced Variational Integrator Networks for Learning and Control of Robot Systems
Valentin Duruisseaux, Thai Duong, Melvin Leok +1
Incorporating prior knowledge of physics laws and structural properties of dynamical systems into the design of deep learning architectures has proven to be a powerful technique fo…
LEMURS: Learning Distributed Multi-Robot Interactions
Eduardo Sebastian, Thai Duong, Nikolay Atanasov +2
This paper presents LEMURS, an algorithm for learning scalable multi-robot control policies from cooperative task demonstrations. We propose a port-Hamiltonian description of the m…
Robust and Safe Autonomous Navigation for Systems with Learned SE(3) Hamiltonian Dynamics
Zhichao Li, Thai Duong, Nikolay Atanasov
Stability and safety are critical properties for successful deployment of automatic control systems. As a motivating example, consider autonomous mobile robot navigation in a compl…
Physics-guided Learning-based Adaptive Control on the SE(3) Manifold
Thai Duong, Nikolay Atanasov
In real-world robotics applications, accurate models of robot dynamics are critical for safe and stable control in rapidly changing operational conditions. This motivates the use o…
Safe Autonomous Navigation for Systems with Learned SE(3) Hamiltonian Dynamics
Zhichao Li, Thai Duong, Nikolay Atanasov
Safe autonomous navigation in unknown environments is an important problem for mobile robots. This paper proposes techniques to learn the dynamics model of a mobile robot from traj…
Adaptive Control of SE(3) Hamiltonian Dynamics with Learned Disturbance Features
Thai Duong, Nikolay Atanasov
Adaptive control is a critical component of reliable robot autonomy in rapidly changing operational conditions. Adaptive control designs benefit from a disturbance model, which is…
Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control
Thai Duong, Nikolay Atanasov
Accurate models of robot dynamics are critical for safe and stable control and generalization to novel operational conditions. Hand-designed models, however, may be insufficiently…
Autonomous Navigation in Unknown Environments with Sparse Bayesian Kernel-based Occupancy Mapping
Thai Duong, Michael Yip, Nikolay Atanasov
This paper focuses on online occupancy mapping and real-time collision checking onboard an autonomous robot navigating in a large unknown environment. Commonly used voxel and octre…
Safe Robot Navigation in Cluttered Environments using Invariant Ellipsoids and a Reference Governor
Zhichao Li, Thai Duong, Nikolay Atanasov
This paper considers the problem of safe autonomous navigation in unknown environments, relying on local obstacle sensing. We consider a control-affine nonlinear robot system subje…
Autonomous Navigation in Unknown Environments using Sparse Kernel-based Occupancy Mapping
Thai Duong, Nikhil Das, Michael Yip +1
This paper focuses on real-time occupancy mapping and collision checking onboard an autonomous robot navigating in an unknown environment. We propose a new map representation, in w…