11 papers
Neural Operator Learning for Collision-Aware Trajectory Planning of Spacecraft Swarms
Sidhdharth D. Sikka, Suyi Gao, Zehui Lu +2
Satellite constellations require orbital transfers that are both fuel efficient and collision avoidant. Yet, the computational cost of optimization methods traditionally used to pl…
Multi-Robot Open Adaptive Teaming Across Unseen Environments, Partners, and Scales
Yang Li, Feng Xue, Fan Mo +6
Deploying robot teams in the real world requires simultaneous adaptation to unseen environments, unknown partners, and varying team sizes, yet existing approaches often address the…
DREAM-Chunk: Reactive Action Chunking with Latent World Model
Wenxi Chen, Kaidi Zhang, Chi Lin +6
Action chunking has become a common interface for vision-language-action (VLA) models, enabling low-frequency policy inference to drive high-frequency robot execution. However, onc…
Efficient Reinforcement Learning using Linear Koopman Dynamics for Nonlinear Robotic Systems
Wenjian Hao, Yuxuan Fang, Zehui Lu +1
This paper presents a model-based reinforcement learning (RL) framework for optimal closed-loop control of nonlinear robotic systems. The proposed approach learns linear lifted dyn…
Online Intention Prediction via Control-Informed Learning
Tianyu Zhou, Zihao Liang, Zehui Lu +1
This paper presents an online intention prediction framework for estimating the goal state of autonomous systems in real time, even when intention is time-varying, and system dynam…
Accelerating Sampling-Based Control via Learned Linear Koopman Dynamics
Wenjian Hao, Yuxuan Fang, Zehui Lu +1
This paper presents an efficient model predictive path integral (MPPI) control framework for systems with complex nonlinear dynamics. To improve the computational efficiency of cla…
Distributed Koopman Learning using Partial Trajectories for Control
Wenjian Hao, Zehui Lu, Devesh Upadhyay +1
This paper proposes a distributed data-driven framework for dynamics learning, termed distributed deep Koopman learning using partial trajectories (DDKL-PT). In this framework, eac…
HOLA-Drone: Hypergraphic Open-ended Learning for Zero-Shot Multi-Drone Cooperative Pursuit
Yang Li, Dengyu Zhang, Junfan Chen +4
Zero-shot coordination (ZSC) is a significant challenge in multi-agent collaboration, aiming to develop agents that can coordinate with unseen partners they have not encountered be…
Distributed Koopman Learning with Incomplete Measurements
Wenjian Hao, Lili Wang, Ayush Rai +1
Koopman operator theory has emerged as a powerful tool for system identification, particularly for approximating nonlinear time-invariant systems (NTIS). This paper considers a net…
Deep Koopman Learning using Noisy Data
Wenjian Hao, Devesh Upadhyay, Shaoshuai Mou
This paper proposes a data-driven framework to learn a finite-dimensional approximation of a Koopman operator for approximating the state evolution of a dynamical system under nois…
C3D: Cascade Control with Change Point Detection and Deep Koopman Learning for Autonomous Surface Vehicles
Jianwen Li, Hyunsang Park, Wenjian Hao +10
In this paper, we discuss the development and deployment of a robust autonomous system capable of performing various tasks in the maritime domain under unknown dynamic conditions.…