Drones and robots. They are becoming increasingly popular in recent years, with technological advances making them more accessible and capable than ever before. We now have a variety of options, from aerial drones used for photography and surveillance to ground-based robots used for manufacturing and logistics. These machines are transforming industries and revolutionizing the way we live and work.
In addition to being fun toys to play with, they are actually a critical component in many tasks. One area where these tools are particularly promising is in the field of autonomous navigation. With the ability to explore and map unknown environments, these machines have the potential to support a wide range of applications, from search and rescue operations to precision agriculture and more.
However, building effective autonomous navigation agents is very challenging, particularly when it comes to exploration. We need to make sure they can operate in unfamiliar environments before we can trust them. They must be able to explore their environment and build accurate maps, all without human intervention or supervision.
Exploring invisible environments is a major challenge in building autonomous navigation agents. There has been a lot of research on training scouting policies to maximize coverage, find specific goals or objects, and support active learning. Modular learning methods have been particularly effective for embedded tasks, as they learn navigation policies that can create semantic maps of the environment for planning and subsequent tasks, such as object or image target navigation.
In parallel, there has been a significant body of work on learning implicit map representations based on Neural Radiance Fields (NeRF), which offer a compact and continuous representation of appearance and semantics in a 3D scene. However, most approaches to constructing implicit representations require human-collected data. But can you imagine if we could build implicit representations without relying on humans? We could send autonomous drones, robots, etc., and map the whole place in 3D. It would be amazing, right?
well let’s get together AutoNeRF. It trains embedded agents to explore invisible environments efficiently and autonomously collect data to generate NeRF. AutoNeRF is a modular policy trained with Reinforcement Learning (RL) that can explore an invisible 3D scene to collect data to autonomously train a NeRF model.
AutoNeRF it allows drones and autonomous robots to collect the data needed to train implicit neural representations of a scene. NeRF serves as a continuous and compact representation of the density, RGB appearance and semantics of the scene. With AutoNeRF, the robot or drone is initialized in an unknown environment and is tasked with collecting data in a single episode within a fixed time budget. Observations collected by the agent during this episode are used to train the NeRF model, which is then evaluated in various downstream tasks in robotics, including mapping, rendering new views, planning, and refining poses.
AutoNeRF has two main phases: Exploration Policy Training and nerf training. During the Exploration Policy Training phase, an exploration policy is trained using intrinsic rewards in a set of training environments. This policy allows the robot or drone to navigate the scene while collecting observations. In it nerf training phase, the scan policy is used to collect data on unseen test scenes, where one trajectory per scene is collected to train the NeRF model. Finally, the trained NeRF model is evaluated in several post tasks to test its effectiveness in embedded AI applications.
One of the key advantages of AutoNeRF is its ability to generate high-quality implicit map renderings using data collected by autonomous agents. This has major implications for a variety of applications, including virtual reality, robotics, and autonomous driving.
review the Paper and Project. Don’t forget to join our 21k+ ML SubReddit, discord channel, and electronic newsletter, where we share the latest AI research news, exciting AI projects, and more. If you have any questions about the article above or if we missed anything, feel free to email us at [email protected]
🚀 Check out 100 AI tools at AI Tools Club
Ekrem Çetinkaya received his B.Sc. in 2018 and M.Sc. in 2019 from Ozyegin University, Istanbul, Türkiye. She wrote her M.Sc. thesis on denoising images using deep convolutional networks. She is currently pursuing a PhD. She graduated from the University of Klagenfurt, Austria, and working as a researcher in the ATHENA project. Her research interests include deep learning, computer vision, and multimedia networks.
