Tactile SLAM: Real-time inference of shape and pose from planar pushing
Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）；Mechanical Engineering Department, Massachusetts Institute of Technology（麻省理工学院机械工程系）；XYZ Robotics（XYZ机器人公司）；Electrical and Computer Engineering Department, Brigham Young University（杨百翰大学电气与计算机工程系）
Sudharshan Suresh, Maria Bauza, Kuan-Ting Yu, Joshua G. Mangelson, Alberto Rodriguez, Michael Kaess
备注：
Camera-ready version to be presented at the 2021 IEEE International Conference on Robotics and Automation (ICRA 2021). For associated video file, see https://youtu.be/wdyagx5MM40
Tactile perception is central to robot manipulation in unstructured environments. However, it requires contact, and a mature implementation must infer object models while also accounting for the motion induced by the interaction. In this work, we present a method to estimate both object shape and pose in real-time from a stream of tactile measurements. This is applied towards tactile exploration of an unknown object by planar pushing. We consider this as an online SLAM problem with a nonparametric shape representation. Our formulation of tactile inference alternates between Gaussian process implicit surface regression and pose estimation on a factor graph. Through a combination of local Gaussian processes and fixed-lag smoothing, we infer object shape and pose in real-time. We evaluate our system across different objects in both simulated and real-world planar pushing tasks.
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…
Contact SLAM: An Active Tactile Exploration Policy Based on Physical Reasoning Utilized in Robotic Fine Blind Manipulation Tasks
Research Center for Intelligent Robotics（智能机器人研究中心）；National Key Laboratory of Aerospace Flight Dynamics（航空飞行动力学国家重点实验室）；Shaanxi Province Innovation Team of Intelligent Robotic Technology（陕西省智能机器人技术创新团队）；School of Astronautics, Northwestern Polytechnical University（航空学院，西北工业大学）
Temple University（天普大学）；Amazon Web Services（亚马逊云科技）；Stony Brook University（石溪大学）
As an essential part of structure from motion (SfM) and Simultaneous Localization and Mapping (SLAM) systems, motion averaging has been extensively studied in the past years and continues to attract surging research attention. While canonical approaches such as bundle adjustment are predominantly inherited in most of state-of-the-art SLAM systems to estimate and update the trajectory in the robot navigation, the practical implementation of bundle adjustment in SLAM systems is intrinsically limited by the high computational complexity, unreliable convergence and strict requirements of ideal ini…
Accurate and robust object state estimation enables successful object manipulation. Visual sensing is widely used to estimate object poses. However, in a cluttered scene or in a tight workspace, the robot's end-effector often occludes the object from the visual sensor. The robot then loses visual feedback and must fall back on open-loop execution. In this paper, we integrate both tactile and visual input using a framework for solving the SLAM problem, incremental smoothing and mapping (iSAM), to provide a fast and flexible solution. Visual sensing provides global pose information but is noisy …
Carnegie Mellon University（卡内基梅隆大学）；UC Santa Barbara（加州大学圣巴巴拉分校）；Facebook AI Research（脸书人工智能研究院）
Intricate behaviors an organism can exhibit is predicated on its ability to sense and effectively interpret complexities of its surroundings. Relevant information is often distributed between multiple modalities, and requires the organism to exhibit information assimilation capabilities in addition to information seeking behaviors. While biological beings leverage multiple sensing modalities for decision making, current robots are overly reliant on visual inputs. In this work, we want to augment our robots with the ability to leverage the (relatively under-explored) modality of touch. To focus…
Tamagawa University（玉川大学）；Advance Intelligence and Robotics Research Center（高级智能与机器人研究中心）
In this work, we present a multimodal system for active robot-object interaction using laser-based SLAM, RGBD images, and contact sensors. In the object manipulation task, the robot adjusts its initial pose with respect to obstacles and target objects through RGBD data so it can perform object grasping in different configuration spaces while avoiding collisions, and updates the information related to the last steps of the manipulation process using the contact sensors in its hand. We perform a series of experiment to evaluate the performance of the proposed system following the the RoboCup2018…
In robots, nonprehensile manipulation operations such as pushing are a useful way of moving large, heavy or unwieldy objects, moving multiple objects at once, or reducing uncertainty in the location or pose of objects. In this study, we propose a reactive and adaptive method for robotic pushing that uses rich feedback from a high-resolution optical tactile sensor to control push movements instead of relying on analytical or data-driven models of push interactions. Specifically, we use goal-driven tactile exploration to actively search for stable pushing configurations that cause the object to …
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）
SLAM assisted 3D tracking system for laparoscopic surgery
United Imaging Research Institute of Intelligent Imaging（联影智能影像研究院）；University of Michigan（密歇根大学）；United Imaging（联影医疗）；United Imaging of Surgery（联影外科研究院）
University of Lincoln（林肯大学）；Politecnico di Milano（米兰理工大学）
This paper introduces a novel approach to address the problem of Physical Robot Interaction (PRI) during robot pushing tasks. The approach uses a data-driven forward model based on tactile predictions to inform the controller about potential future movements of the object being pushed, such as a strawberry stem, using a robot tactile finger. The model is integrated into a Deep Functional Predictive Control (d-FPC) system to control the displacement of the stem on the tactile finger during pushes. Pushing an object with a robot finger along a desired trajectory in 3D is a highly nonlinear and c…
Facebook AI Research（Facebook人工智能研究院）；Carnegie Mellon University（卡内基梅隆大学）
We're interested in the problem of estimating object states from touch during manipulation under occlusions. In this work, we address the problem of estimating object poses from touch during planar pushing. Vision-based tactile sensors provide rich, local image measurements at the point of contact. A single such measurement, however, contains limited information and multiple measurements are needed to infer latent object state. We solve this inference problem using a factor graph. In order to incorporate tactile measurements in the graph, we need local observation models that can map high-dime…
University of Bristol（布里斯托大学）；Bristol Robotics Laboratory（布里斯托机器人实验室）
High-resolution optical tactile sensors are increasingly used in robotic learning environments due to their ability to capture large amounts of data directly relating to agent-environment interaction. However, there is a high barrier of entry to research in this area due to the high cost of tactile robot platforms, specialised simulation software, and sim-to-real methods that lack generality across different sensors. In this letter we extend the Tactile Gym simulator to include three new optical tactile sensors (TacTip, DIGIT and DigiTac) of the two most popular types, Gelsight-style (image-sh…
Georgia Institute of Technology（佐治亚理工学院）；NVIDIA（英伟达）；University of Utah（犹他大学）；University of Washington（华盛顿大学）
To perform complex tasks, robots must be able to interact with and manipulate their surroundings. One of the key challenges in accomplishing this is robust state estimation during physical interactions, where the state involves not only the robot and the object being manipulated, but also the state of the contact itself. In this work, within the context of planar pushing, we extend previous inference-based approaches to state estimation in several ways. We estimate the robot, object, and the contact state on multiple manipulation platforms configured with a vision-based articulated model track…
Distributed Cascade Force Control of Soft-Tactile-Based Multi-robot System for Object Transportation
University of Prince Edward Island（爱德华王子岛大学）；Japan Advanced Institute of Science and Technology（北陆先端科学技术大学院大学）；University of Engineering and Technology, Vietnam（越南工程技术大学）；Japan Science and Technology Agency（日本科学技术振兴机构）
Tactile Active Inference Reinforcement Learning for Efficient Robotic Manipulation Skill Acquisition
Research Center for Intelligent Robotics, School of Astronautics, Northwestern Polytechnical University（西北工业大学航天学院智能机器人研究中心）；National Key Laboratory of Aerospace Flight Dynamics, Northwestern Polytechnical University（西北工业大学航空航天飞行动力学全国重点实验室）
DNPush: Distributed Nonparallel Pushing Force Control of Multi-Robot Systems for Object Transportation
More-Than-One Robotics Laboratory, Faculty of Sustainable Design Engineering, University of Prince Edward Island（爱德华王子岛大学可持续设计工程学院多机器人实验室）；Faculty of Electronics and Telecommunications, VNU University of Engineering and Technology（越南国立大学工程与技术大学电子与电信学院）
