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03/19/2022
Incremental Few-Shot Learning via Implanting and Compressing
This work focuses on tackling the challenging but realistic visual task ...
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02/20/2022
Velocity Obstacle Based Risk-Bounded Motion Planning for Stochastic Multi-Agent Systems
In this paper, we present an innovative risk-bounded motion planning met...
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03/24/2021
Receding Horizon Motion Planning for Multi-Agent Systems: A Velocity Obstacle Based Probabilistic Method
In this paper, a novel and innovative methodology for feasible motion pl...
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03/23/2021
Generalized Iterative Super-Twisting Sliding Mode Control: A Case Study on Flexure-Joint Dual-Drive H-Gantry Stage
Mechatronic systems are commonly used in the industry, where fast and ac...
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01/25/2021
ADMM-Based Parallel Optimization for Multi-Agent Collision-Free Model Predictive Control
This paper investigates the multi-agent collision-free control problem f...
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01/01/2021
Sequential Convex Programming for Collaboration of Connected and Automated Vehicles
This paper investigates the collaboration of multiple connected and auto...
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01/01/2021
Semi-Definite Relaxation Based ADMM for Cooperative Planning and Control of Connected Autonomous Vehicles
This paper investigates the cooperative planning and control problem for...
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11/21/2020
Neural Network iLQR: A New Reinforcement Learning Architecture
As a notable machine learning paradigm, the research efforts in the cont...
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11/06/2020
Data-Driven Predictive Control for Multi-Agent Decision Making With Chance Constraints
In the recent literature, significant and substantial efforts have been ...
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11/01/2020
Hierarchical ADMM for Nonconvex Cooperative Distributed Model Predictive Control
Distributed optimization is often widely attempted and innovated as an a...
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11/01/2020