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===Safe Reinforcement Learning=== | ===Safe Reinforcement Learning=== | ||
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Investigation on Safe RL algorithm performance on a realistic industrial robot (a Driveable Vertical Mast Lift). | Investigation on Safe RL algorithm performance on a realistic industrial robot (a Driveable Vertical Mast Lift). | ||
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[[GTMillinoise | Read more]] | [[GTMillinoise | Read more]] | ||
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| + | ===APEIRON: a Multimodal Drone Dataset Bridging Perception and Network Data=== | ||
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| + | APEIRON is a multimodal aerial dataset bridging the gap between perception and network data in outdoor environments, fostering multidisciplinary research. | ||
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| + | [[Apeiron | Read more]] | ||
Autonomy and Perception are two key aspects of modern robotics. Our work spans both of these areas. In particular, we employ vision and radar technologies and apply them to the development of autonomous robots. Regarding autonomy, we use both classical and modern control methods, as well as reinforcement learning.
Improve RL sample efficiency with two new tools: Episodic Noise and Difficulty Manager.
Investigation on Safe RL algorithm performance on a realistic industrial robot (a Driveable Vertical Mast Lift).
In this work we designed a contrastive learning-based technique to translate mmWave radar point clouds to depth images with Point2Depth.
GT-MilliNoise is a learning-based denoiser for indoor millimetere-wave radars point cloud. We also released MilliNoise a dataset that collects mmWave radar point cloud and labels each point with a sub-millimetric accuracy.
APEIRON is a multimodal aerial dataset bridging the gap between perception and network data in outdoor environments, fostering multidisciplinary research.
Autonomy and Perception are two key aspects of modern robotics. Our work spans both of these areas. In particular, we employ vision and radar technologies and apply them to the development of autonomous robots. Regarding autonomy, we use both classical and modern control methods, as well as reinforcement learning.
Improve RL sample efficiency with two new tools: Episodic Noise and Difficulty Manager.
Investigation on Safe RL algorithm performance on a realistic industrial robot (a Driveable Vertical Mast Lift).
In this work we designed a contrastive learning-based technique to translate mmWave radar point clouds to depth images with Point2Depth.
GT-MilliNoise is a learning-based denoiser for indoor millimetere-wave radars point cloud. We also released MilliNoise a dataset that collects mmWave radar point cloud and labels each point with a sub-millimetric accuracy.