14 citations · 33 across the 7 of their papers we have counts for
9 papers · 1 filter
Improving Pedestrian Prediction Models with Self-Supervised Continual Learning
Luzia Knoedler, Chadi Salmi, Hai Zhu +2
Autonomous mobile robots require accurate human motion predictions to safely and efficiently navigate among pedestrians, whose behavior may adapt to environmental changes. This pap…
Decentralized Probabilistic Multi-Robot Collision Avoidance Using Buffered Uncertainty-Aware Voronoi Cells
Hai Zhu, Bruno Brito, Javier Alonso-Mora
In this paper, we present a decentralized and communication-free collision avoidance approach for multi-robot systems that accounts for both robot localization and sensing uncertai…
Learning Interaction-aware Guidance Policies for Motion Planning in Dense Traffic Scenarios
Bruno Brito, Achin Agarwal, Javier Alonso-Mora
Autonomous navigation in dense traffic scenarios remains challenging for autonomous vehicles (AVs) because the intentions of other drivers are not directly observable and AVs have…
Scenario-Based Trajectory Optimization in Uncertain Dynamic Environments
O. de Groot, B. Brito, L. Ferranti +2
We present an optimization-based method to plan the motion of an autonomous robot under the uncertainties associated with dynamic obstacles, such as humans. Our method bounds the m…
Where to go next: Learning a Subgoal Recommendation Policy for Navigation Among Pedestrians
Bruno Brito, Michael Everett, Jonathan P. How +1
Robotic navigation in environments shared with other robots or humans remains challenging because the intentions of the surrounding agents are not directly observable and the envir…
Learning Interaction-Aware Trajectory Predictions for Decentralized Multi-Robot Motion Planning in Dynamic Environments
Hai Zhu, Francisco Martinez Claramunt, Bruno Brito +1
This paper presents a data-driven decentralized trajectory optimization approach for multi-robot motion planning in dynamic environments. When navigating in a shared space, each ro…