AU Dataset for Visuo-Haptic Object Recognition for Robots
Multimodal object recognition is still an emerging field. Thus, publicly available datasets are still rare and of small size. This dataset was developed to help fill this void and presents multimodal data for 63 objects with some visual and haptic ambiguity. The dataset contains visual, kinesthetic and tactile (audio/vibrations) data. To completely solve sensory ambiguity, sensory integration/fusion would be required. This report describes the creation and structure of the dataset. The first section explains the underlying approach used to capture the visual and haptic properties of the objects. The second section describes the technical aspects (experimental setup) needed for the collection of the data. The third section introduces the objects, while the final section describes the structure and content of the dataset.
Code (0)
등록된 구현이 없습니다.
Tasks
ObjectObject RecognitionSimilar Papers 제목 키워드 기반
Visuo-Haptic Object Perception for Robots: An Overview
The object perception capabilities of humans are impressive, and this becomes even more evident when trying to develop solutions with a similar proficiency in autonomous robots. While there have been notable advancements…
ArticlesObjectObject RecognitionTransfer LearningA Digital Twin Framework for Virtual Visuo-Haptic Teleoperation of Complex-Shaped Optical Microrobots
Optical tweezers (OT) provide piconewton-scale manipulation for delicate biomedical tasks, where visuo-haptic feedback can improve operator awareness by conveying interaction-force cues and trap-stability information. Ho…
Depth EstimationV-HOP: Visuo-Haptic 6D Object Pose Tracking
Humans naturally integrate vision and haptics for robust object perception during manipulation. The loss of either modality significantly degrades performance. Inspired by this multisensory integration, prior object pose…
ObjectObject TrackingPose EstimationPose TrackingProbabilistic Surface Friction Estimation Based on Visual and Haptic Measurements
Accurately modeling local surface properties of objects is crucial to many robotic applications, from grasping to material recognition. Surface properties like friction are however difficult to estimate, as visual observ…
FrictionMaterial RecognitionObjectSeeing by haptic glance: reinforcement learning-based 3D object Recognition
Human is able to conduct 3D recognition by a limited number of haptic contacts between the target object and his/her fingers without seeing the object. This capability is defined as `haptic glance' in cognitive neuroscie…
3D Object RecognitionObjectObject Recognitionreinforcement-learning+2