Tactile information plays a crucial role in human manipulation tasks and has recently garnered increasing attention in robotic manipulation. However, existing approaches mostly focus on the alignment of visual and tactile features and the integration mechanism tends to be direct concatenation. Consequently, they struggle to effectively cope with occluded scenarios due to neglecting the inherent complementary nature of both modalities and the alignment may not be exploited enough, limiting the potential of their real-world deployment. In this paper, we present ViTaS, a simple yet effective framework that incorporates both visual and tactile information to guide the behavior of an agent. We introduce Soft Fusion Contrastive Learning, an advanced version of conventional contrastive learning method and a CVAE module to utilize the alignment and complementarity within visuo-tactile representations. We demonstrate the effectiveness of our method in 12 simulated and 3 real-world environments, and our experiments show that ViTaS significantly outperforms existing baselines. Project page: https://skyrainwind.github.io/ViTaS/index.html.
KineDex: Learning Tactile-Informed Visuomotor Policies via Kinesthetic Teaching for Dexterous Manipulation
Tongji University（同济大学）；Tsinghua University（清华大学）；Shanghai Jiao Tong University（上海交通大学）；University of Hong Kong（香港大学）；Shanghai Qi Zhi Institute（上海启智研究院）；Shanghai AI Lab（上海人工智能实验室）
ViTacGen: Robotic Pushing with Vision-to-Touch Generation
Department of Engineering, King’s College London（工程系，伦敦国王学院）；Department of Engineering Mathematics and Bristol, Robotics Laboratory, University of Bristol（工程数学系及布里斯托尔机器人实验室，布里斯托尔大学）
ViTaPEs: Visuotactile Position Encodings for Cross-Modal Alignment in Multimodal Transformers
Chair of Cyber-Physical Systems（感知系统教授席位）；Institute of Machine Learning and Neural Computation（机器学习与神经计算研究所）；Technical University of Leoben（莱比锡技术大学）；Graz University of Technology（格拉茨技术大学）
Canonical Representation and Force-Based Pretraining of 3D Tactile for Dexterous Visuo-Tactile Policy Learning
Center on Frontiers of Computing Studies, School of Computer Science, Peking University（前沿计算研究中心，计算机科学学院，北京大学）；PKU-Agibot Lab, School of Computer Science, Peking University（北京大学计算机科学学院PKU-Agibot实验室）；National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University（国家多媒体信息处理重点实验室，计算机科学学院，北京大学）
TouchGuide: Inference-Time Steering of Visuomotor Policies via Touch Guidance
Shanghai Jiao Tong University（上海交通大学）；Xense Robotics（Xense机器人公司）；Sun Yat-sen University（中山大学）；Oxford（牛津大学）；Shanghai AI Laboratory（上海人工智能实验室）；University of Science and Technology of China（中国科学技术大学）；UCSD（加州大学圣地亚哥分校）
Computer Science and Artificial Intelligence Laboratory (CSAIL), MIT（麻省理工学院计算机科学与人工智能实验室（CSAIL））
Slip detection plays a vital role in robotic manipulation and it has long been a challenging problem in the robotic community. In this paper, we propose a new method based on deep neural network (DNN) to detect slip. The training data is acquired by a GelSight tactile sensor and a camera mounted on a gripper when we use a robot arm to grasp and lift 94 daily objects with different grasping forces and grasping positions. The DNN is trained to classify whether a slip occurred or not. To evaluate the performance of the DNN, we test 10 unseen objects in 152 grasps. A detection accuracy as high as …
In this work, we propose a novel, bio-inspired multi-sensory SLAM approach called ViTa-SLAM. Compared to other multisensory SLAM variants, this approach allows for a seamless multi-sensory information fusion whilst naturally interacting with the environment. The algorithm is empirically evaluated in a simulated setting using a biomimetic robot platform called the WhiskEye. Our results show promising performance enhancements over existing bio-inspired SLAM approaches in terms of loop-closure detection.
Aalto University（阿尔托大学）；University of Bristol（布里斯托大学）；University of the West of England（西英格兰大学）
RatSLAM is a rat hippocampus-inspired visual Simultaneous Localization and Mapping (SLAM) framework capable of generating semi-metric topological representations of indoor and outdoor environments. Whisker-RatSLAM is a 6D extension of the RatSLAM and primarily focuses on object recognition by generating point clouds of objects based on whisking information. This paper introduces a novel extension to both former works that is referred to as ViTa-SLAM that harnesses both vision and tactile information for performing SLAM. This not only allows the robot to perform natural interaction with the env…
ViTac-Tracing: Visual-Tactile Imitation Learning of Deformable Object Tracing
Robot Perception Laboratory, Department of Engineering, King’s College London（机器人感知实验室，工程系，伦敦国王学院）；Personal Robotics Laboratory, Department of Electrical and Electronic Engineering, Imperial College London（个人机器人实验室，电子与电气工程系，伦敦帝国学院）
Department of Computer Science, University of Liverpool（利物浦大学计算机科学系）
The integration of visual-tactile stimulus is common while humans performing daily tasks. In contrast, using unimodal visual or tactile perception limits the perceivable dimensionality of a subject. However, it remains a challenge to integrate the visual and tactile perception to facilitate robotic tasks. In this paper, we propose a novel framework for the cross-modal sensory data generation for visual and tactile perception. Taking texture perception as an example, we apply conditional generative adversarial networks to generate pseudo visual images or tactile outputs from data of the other m…
Discrete Policy: Learning Disentangled Action Space for Multi-Task Robotic Manipulation
Syracuse University（雪城大学）；Midea Group（美的集团）；Shanghai University（上海大学）；East China Normal University（华东师范大学）；Beijing Innovation Center of Humanoid Robotics（北京人形机器人创新中心）
Placing by Touching: An empirical study on the importance of tactile sensing for precise object placing
Institut de Robòtica i Informàtica Industrial, CSIC-UPC（西班牙国家研究委员会-加泰罗尼亚理工大学工业机器人与信息技术研究所）；Bielefeld University（比勒费尔德大学）；PAL Robotics（PAL机器人公司）；Technical University 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）；Centre for Cognitive Science（认知科学中心）
