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Vision-based Dynamic Obstacle Avoidance
Dynamic obstacle avoidance is a grand challenge in vision-based drone navigation. The classical mapping-planning-control pipeline might have difficulties when facing dynamic objects.
Dynamic obstacle avoidance is a grand challenge in vision-based drone navigation. The classical mapping-planning-control pipeline might have difficulties when facing dynamic objects. Learning-based systems, such as end-to-end neural network policies, are gaining popularity in robotics for dynamic objects, due to their powerful performance and versatility in handling high-dimensional state representations. Particularly, deep reinforcement learning allows for optimizing neural network policies via trial-and-error, forgoing the need for demonstrations.
Dynamic obstacle avoidance is a grand challenge in vision-based drone navigation. The classical mapping-planning-control pipeline might have difficulties when facing dynamic objects. Learning-based systems, such as end-to-end neural network policies, are gaining popularity in robotics for dynamic objects, due to their powerful performance and versatility in handling high-dimensional state representations. Particularly, deep reinforcement learning allows for optimizing neural network policies via trial-and-error, forgoing the need for demonstrations.
The goal is to develop an autonomous vision-based navigation system that can avoid dynamic obstacles using deep reinforcement learning. Applicants should have strong experience in C++ and python programming. Reinforcement learning and robotics background are required.
The goal is to develop an autonomous vision-based navigation system that can avoid dynamic obstacles using deep reinforcement learning. Applicants should have strong experience in C++ and python programming. Reinforcement learning and robotics background are required.