Mobile robot navigation and path planning.
Luca Zanatta, Grzegorz Malczyk, Kostas Alexis
World models, learned generative models that predict how an environment evolves, have become a promising tool for sample-efficient robot learning. Yet how robust they are to environmental variability remains poorly understood.
Haoxuan Han, Chen Chen, Linao Gong et al.
Humanoid robots have achieved strong locomotion capabilities, but reliable navigation on versatile terrains remains challenging because obstacle avoidance must be coordinated with dynamically feasible motion.
Marco Coscoy, Zewei Zhou, Seth Z. Zhao et al.
Vehicle-to-Everything (V2X) communication has emerged as a promising paradigm for autonomous driving, enabling connected agents to share complementary perception information and negotiate with each other to benefit the final planning.
Qiuyue Wang, Mingsheng Li, Jian Guan et al.
Embodied intelligence is often studied through specialized models for individual tasks such as manipulation or navigation, resulting in fragmented capabilities and limited generalization across tasks,
Haechan Mark Bong, Giovanni Beltrame
Safe autonomous Uncrewed Aerial Vehicle (UAV) navigation in urban environments requires real-time path planning that avoids obstacles. MaxConvNet is a potential-field planner that leverages properties of Maxwell's equations to generate a path to the goal without local minima.
Read these papers in order to build expertise in Navigation.
Luca Zanatta, Grzegorz Malczyk et al. · 2026
Haoxuan Han, Chen Chen et al. · 2026
Marco Coscoy, Zewei Zhou et al. · 2026
Qiuyue Wang, Mingsheng Li et al. · 2026
Haechan Mark Bong, Giovanni Beltrame · 2026
