The recent development in autonomous driving involves high-level computer vision and detailed road scene understanding.Today,most autonomous vehicles employ expensive high quality sensor-set such as light detection an...The recent development in autonomous driving involves high-level computer vision and detailed road scene understanding.Today,most autonomous vehicles employ expensive high quality sensor-set such as light detection and ranging(LIDAR)and HD maps with high level annotations.In this paper,we propose a scalable and affordable data collection and annotation framework image-to-map annotation proximity(I2MAP),for affordance learning in autonomous driving applications.We provide a new driving dataset using our proposed framework for driving scene affordance learning by calibrating the data samples with available tags from online database such as open street map(OSM).Our benchmark consists of 40000 images with more than40 affordance labels under various day time and weather even with very challenging heavy snow.We implemented sample advanced driver-assistance systems(ADAS)functions by training our data with neural networks(NN)and cross-validate the results on benchmarks like KITTI and BDD100K,which indicate the effectiveness of our framework and training models.展开更多
Embodied learning for object-centric robotic manipulation is a rapidly developing and challenging area in embodied AI.It is crucial for advancing next-generation intelligent robots and has garnered significant interes...Embodied learning for object-centric robotic manipulation is a rapidly developing and challenging area in embodied AI.It is crucial for advancing next-generation intelligent robots and has garnered significant interest recently.Unlike data-driven machine learning methods,embodied learning focuses on robot learning through physical interaction with the environment and perceptual feedback,making it especially suitable for robotic manipulation.In this paper,we provide a comprehensive survey of the latest advancements in this field and categorize the existing work into three main branches:1)Embodied perceptual learning,which aims to predict object pose and affordance through various data representations;2)Embodied policy learning,which focuses on generating optimal robotic decisions using methods such as reinforcement learning and imitation learning;3)Embodied task-oriented learning,designed to optimize the robot′s performance based on the characteristics of different tasks in object grasping and manipulation.In addition,we offer an overview and discussion of public datasets,evaluation metrics,representative applications,current challenges,and potential future research directions.A project associated with this survey has been established at https://github.com/RayYoh/OCRM_survey.展开更多
文摘The recent development in autonomous driving involves high-level computer vision and detailed road scene understanding.Today,most autonomous vehicles employ expensive high quality sensor-set such as light detection and ranging(LIDAR)and HD maps with high level annotations.In this paper,we propose a scalable and affordable data collection and annotation framework image-to-map annotation proximity(I2MAP),for affordance learning in autonomous driving applications.We provide a new driving dataset using our proposed framework for driving scene affordance learning by calibrating the data samples with available tags from online database such as open street map(OSM).Our benchmark consists of 40000 images with more than40 affordance labels under various day time and weather even with very challenging heavy snow.We implemented sample advanced driver-assistance systems(ADAS)functions by training our data with neural networks(NN)and cross-validate the results on benchmarks like KITTI and BDD100K,which indicate the effectiveness of our framework and training models.
基金supported in part by the National Natural Science Foundation of China(No.62106236).
文摘Embodied learning for object-centric robotic manipulation is a rapidly developing and challenging area in embodied AI.It is crucial for advancing next-generation intelligent robots and has garnered significant interest recently.Unlike data-driven machine learning methods,embodied learning focuses on robot learning through physical interaction with the environment and perceptual feedback,making it especially suitable for robotic manipulation.In this paper,we provide a comprehensive survey of the latest advancements in this field and categorize the existing work into three main branches:1)Embodied perceptual learning,which aims to predict object pose and affordance through various data representations;2)Embodied policy learning,which focuses on generating optimal robotic decisions using methods such as reinforcement learning and imitation learning;3)Embodied task-oriented learning,designed to optimize the robot′s performance based on the characteristics of different tasks in object grasping and manipulation.In addition,we offer an overview and discussion of public datasets,evaluation metrics,representative applications,current challenges,and potential future research directions.A project associated with this survey has been established at https://github.com/RayYoh/OCRM_survey.
