Recent advancements in object shape completion have enabled impressive object reconstructions using only visual input. However, due to self-occlusion, the reconstructions have high uncertainty in the occluded object parts, which negatively impacts the performance of downstream robotic tasks such as grasping. In this work, we propose an active visuo-haptic shape completion method called Act-VH that actively computes where to touch the objects based on the reconstruction uncertainty. Act-VH reconstructs objects from point clouds and calculates the reconstruction uncertainty using IGR, a recent state-of-the-art implicit surface deep neural network. We experimentally evaluate the reconstruction accuracy of Act-VH against five baselines in simulation and in the real world. We also propose a new simulation environment for this purpose. The results show that Act-VH outperforms all baselines and that an uncertainty-driven haptic exploration policy leads to higher reconstruction accuracy than a random policy and a policy driven by Gaussian Process Implicit Surfaces. As a final experiment, we evaluate Act-VH and the best reconstruction baseline on grasping 10 novel objects. The results show that Act-VH reaches a significantly higher grasp success rate than the baseline on all objects. Together, this work opens up the door for using active visuo-haptic shape completion in more complex cluttered scenes.
Facebook AI Research（Facebook人工智能研究院）；McGill University（麦吉尔大学）；University of California, Berkeley（加州大学伯克利分校）
Humans build 3D understandings of the world through active object exploration, using jointly their senses of vision and touch. However, in 3D shape reconstruction, most recent progress has relied on static datasets of limited sensory data such as RGB images, depth maps or haptic readings, leaving the active exploration of the shape largely unexplored. Inactive touch sensing for 3D reconstruction, the goal is to actively select the tactile readings that maximize the improvement in shape reconstruction accuracy. However, the development of deep learning-based active touch models is largely limit…
Armstrong Atlantic State Univ.（阿姆斯特朗大西洋州立大学）；Ovidius University（奥维迪乌斯大学）
Surgeons must accomplish complex technical and intellectual tasks that can generate unexpected and serious challenges with little or no room for error. In the last decade, computer simulations have played an increasing role in surgical training, pre-operative planning, and biomedical research. Specifically, visuo-haptic simulations have been the focus of research to develop advanced e-Learning systems facilitating surgical training. The cost of haptic hardware was reduced through mass scale production and as haptics gained popularity in the gaming industry. Visuo-haptic simulations combine the…
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 object…
Integrating and Evaluating Visuo-tactile Sensing with Haptic Feedback for Teleoperated Robot Manipulation
Technische Universität Darmstadt（达姆施塔特工业大学）；German Research Center for AI (DFKI)（德国人工智能研究中心）；Hessian Centre for Artificial Intelligence (Hessian.AI)（黑森州人工智能中心）；Centre for Cognitive Science (CogSci)（认知科学中心）；Nagoya University（名古屋大学）
Acquiring food items with a fork poses an immense challenge to a robot-assisted feeding system, due to the wide range of material properties and visual appearances present across food groups. Deformable foods necessitate different skewering strategies than firm ones, but inferring such characteristics for several previously unseen items on a plate remains nontrivial. Our key insight is to leverage visual and haptic observations during interaction with an item to rapidly and reactively plan skewering motions. We learn a generalizable, multimodal representation for a food item from raw sensory i…
Understanding Particles From Video: Property Estimation of Granular Materials via Visuo-Haptic Learning
The University of Hong Kong（香港大学）；Center for Transformative Garment Production（变革性服装生产中心）；Southern University of Science and Technology（南方科技大学）；Baidu Research（百度研究院）
Technical University of Munich（慕尼黑工业大学）；ABB Corporate Research（ABB集团研究院）；University of Campania Luigi Vanvitelli（坎帕尼亚大学路易吉·万维泰利）；Institute of Robotics and Mechatronics, German Aerospace Center（德国航空航天中心机器人与机电一体化研究所）
In this work, we introduce the problem of cross-modal visuo-tactile object recognition with robotic active exploration. With this term, we mean that the robot observes a set of objects with visual perception and, later on, it is able to recognize such objects only with tactile exploration, without having touched any object before. Using a machine learning terminology, in our application we have a visual training set and a tactile test set, or vice versa. To tackle this problem, we propose an approach constituted by four steps: finding a visuo-tactile common representation, defining a suitable …
Spacetime Optimal-Transport Attention for Visuo-Haptic Imitation Learning of Contact-Rich Manipulation
Robotics Research Centre, School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore（机器人研究中心，机械与航空航天工程学院，南洋理工大学，新加坡）；Wings Robotics（Wings机器人）
University of Heidelberg（海德堡大学）；University of Genoa（热那亚大学）；University Niccolò Cusano（尼可洛·库萨诺大学）
Bimanual object manipulation involves multiple visuo-haptic sensory feedbacks arising from the interaction with the environment that are managed from the central nervous system and consequently translated in motor commands. Kinematic strategies that occur during bimanual coupled tasks are still a scientific debate despite modern advances in haptics and robotics. Current technologies may have the potential to provide realistic scenarios involving the entire upper limb extremities during multi-joint movements but are not yet exploited to their full potential. The present study explores how hands…
Technical University of Munich（慕尼黑工业大学）；Imperial College London（帝国理工学院）；University of Bayreuth（拜罗伊特大学）
Intuitive and efficient physical human-robot collaboration relies on the mutual observability of the human and the robot, i.e. the two entities being able to interpret each other's intentions and actions. This is remedied by a myriad of methods involving human sensing or intention decoding, as well as human-robot turn-taking and sequential task planning. However, the physical interaction establishes a rich channel of communication through forces, torques and haptics in general, which is often overlooked in industrial implementations of human-robot interaction. In this work, we investigate the …
Leveraging Tactile Sensing to Render both Haptic Feedback and Virtual Reality 3D Object Reconstruction in Robotic Telemanipulation
Queen Mary University of London（伦敦玛丽女王大学）；School of Engineering and Materials Science（工程与材料科学学院）；Humanoid AI（人形人工智能公司）；King’s College London（伦敦国王学院）；School of Biomedical Engineering and Imaging Sciences（生物医学工程与影像科学学院）
SoftVTBench: A Safety-Aware Visuo-Tactile Benchmark for Physically Constrained Robotic Manipulation of Deformable Objects (Early Version)
Tuojing Intelligence（拓景智能）；Tsinghua University（清华大学）；King’s College London（伦敦国王学院）；Southeast University（东南大学）；Stevens Institute of Technology（史蒂文斯理工学院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of Manchester（曼彻斯特大学）；Simple AI（简单人工智能公司）；Imperial College London（伦敦帝国学院）；Carnegie Mellon University（卡内基梅隆大学）；Zhejiang University（浙江大学）；Beihang University（北京航空航天大学）；The University of Hong Kong（香港大学）
