Learning to Control Drones in Natural Environments: A Survey
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The work of this thesis centers around the research subject of distributed robotic search. Within the field of distributed robotic systems, a task of particular interest is attempting to locate one or more targets in a possibly unknown environment. While n ...
Designing effective behavioral controllers for mobile robots can be difficult and tedious; this process can be circumvented by using unsupervised learning techniques which allow robots to evolve their own controllers online in an automated fashion. In mult ...
Optic-flow is believed to be the main source of information allowing insects to control their flight. Some researchers have tried to apply this paradigm to small unmanned aerial vehicles (UAVs). So far, none of them has been able to demonstrate a fully aut ...
Designing effective behavioral controllers for mobile robots can be difficult and tedious; this process can be circumvented by using unsupervised learning techniques which allow robots to evolve their own controllers in an automated fashion. In multi-robot ...
We apply an adapted version of Particle Swarm Optimization to distributed unsupervised robotic learning in groups of robots with only local information. The performance of the learning technique for a simple task is compared across robot groups of various ...
Agile autonomous systems are emerging, such as unmanned aerial vehicles (UAVs), that must robustly perform tightly coordinated time-critical missions; for example, military surveillance or search-and-rescue scenarios. In the space domain, execution of temp ...
In order to verify models of collective behaviors of animals, robots could be manipulated to implement the model and interact with real animals in a mixed-society. This thesis describes design of the behavioral hierarchy of a miniature robot, that is able ...
Mobility in an effective and socially-compliant manner is an essential yet challenging task for robots operating in crowded spaces. Recent works have shown the power of deep reinforcement learning techniques to learn socially cooperative policies. However, ...
Using each other's knowledge and expertise in learning - what we call cooperation in learning- is one of the major existing methods to reduce the number of learning trials, which is quite crucial for real world applications. In situated systems, robots bec ...