Granular media (e.g., cereal grains, plastic resin pellets, and pills) are ubiquitous in robotics-integrated industries, such as agriculture, manufacturing, and pharmaceutical development. This prevalence mandates the accurate and efficient simulation of these materials. This work presents a software and hardware framework that automatically calibrates a fast physics simulator to accurately simulate granular materials by inferring material properties from real-world depth images of granular formations (i.e., piles and rings). Specifically, coefficients of sliding friction, rolling friction, and restitution of grains are estimated from summary statistics of grain formations using likelihood-free Bayesian inference. The calibrated simulator accurately predicts unseen granular formations in both simulation and experiment; furthermore, simulator predictions are shown to generalize to more complex tasks, including using a robot to pour grains into a bowl, as well as to create a desired pattern of piles and rings. Visualizations of the framework and experiments can be viewed at https://youtu.be/OBvV5h2NMKA
University of Washington（华盛顿大学）；Google, Inc.（谷歌公司）；University of California Berkeley（加州大学伯克利分校）
In this paper, we examine the problem of robotic manipulation of granular media. We evaluate multiple predictive models used to infer the dynamics of scooping and dumping actions. These models are evaluated on a task that involves manipulating the media in order to deform it into a desired shape. Our best performing model is based on a highly-tailored convolutional network architecture with domain-specific optimizations, which we show accurately models the physical interaction of the robotic scoop with the underlying media. We empirically demonstrate that explicitly predicting physical mechani…
CSIRO（联邦科学与工业研究组织）；University of Queensland（昆士兰大学）；Queensland University of Technology（昆士兰科技大学）；University of Melbourne（墨尔本大学）
We test grip strength and shock absorption properties of various granular material in granular jamming robotic components. The granular material comprises a range of natural, manufactured, and 3D printed material encompassing a wide range of shapes, sizes, and Shore hardness. Two main experiments are considered, both representing compelling use cases for granular jamming in soft robotics. The first experiment measures grip strength (retention force measured in Newtons) when we fill a latex balloon with the chosen grain type and use it as a granular jamming gripper to pick up a range of test ob…
RoboCAP: Robotic Classification and Precision Pouring of Diverse Liquids and Granular Media with Capacitive Sensing
Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）；Department of Computer Science, Colby College（科尔比学院计算机系）；Department of Computer Science, University of Virginia（弗吉尼亚大学计算机系）
Inferring Operator Emotions from a Motion-Controlled Robotic Arm
School of Engineering, University of Glasgow（格拉斯哥大学工程学院）；Lyle School of Engineering, Southern Methodist University（南方 Methodist 大学莱尔工程学院）；School of Computing, University of Glasgow（格拉斯哥大学计算学院）；University of Manchester（曼彻斯特大学）
Finger-tip tactile sensors are increasingly used for robotic sensing to establish stable grasps and to infer object properties. Promising performance has been shown in a number of works for inferring adjectives that describe the object, but there remains a question about how each taxel contributes to the performance. This paper explores this question with empirical experiments, leading insights for future finger-tip tactile sensor usage and design.
Robot Excavation and Manipulation of Geometrically Cohesive Granular Media
Department of Mechanical Engineering（机械工程系）；University of Vermont（佛蒙特大学）；School of Physics（物理系）；Georgia Institute of Technology（佐治亚理工学院）；George W. Woodruff School of Mechanical Engineering（乔治·W·伍德鲁夫机械工程学院）；School of Biological Sciences（生物科学系）
Data61, CSIRO（澳大利亚联邦科学与工业研究组织数据61中心）；University of Queensland（昆士兰大学）
Granular jamming is a popular soft robotics technology that has seen recent widespread applications including industrial gripping, surgical robotics and haptics. However, to date the field has not fully exploited the fundamental science of the jamming phase transition, which has been rigorously studied in the field of statistical and condensed matter physics. This work introduces vibration as a means to improve the properties of granular jamming grippers through vibratory fluidisation and the exploitation of resonant modes within the granular material. We show that vibration in soft jamming gr…
CSIRO（联邦科学与工业研究组织）；Queensland University of Technology（昆士兰科技大学）；University of Queensland（昆士兰大学）
Granular jamming has recently become popular in soft robotics with widespread applications including industrial gripping, surgical robotics and haptics. Previous work has investigated the use of various techniques that exploit the nature of granular physics to improve jamming performance, however this is generally underrepresented in the literature compared to its potential impact. We present the first research that exploits vibration-based fluidisation actively (e.g., during a grip) to elicit bespoke performance from granular jamming grippers. We augment a conventional universal gripper with …
CSIRO（澳大利亚联邦科学与工业研究组织）；University of Queensland（昆士兰大学）；Queensland University of Technology（昆士兰科技大学）；University of Melbourne（墨尔本大学）
The application of granular jamming in soft robotics is a recent and promising new technology offer exciting possibilities for creating higher performance robotic devices. Granular jamming is achieved via the application of a vacuum pressure inside a membrane containing particulate matter, and is particularly interesting from a design perspective, as a myriad of design parameters can potentially be exploited to induce a diverse variety of useful behaviours. To date, the effect of variables such as grain shape and size, as well as membrane material, have been studied as a means of inducing besp…
Department of Electrical；Computer Engineering, Concorida University, Canada
An important challenge in robotics is understanding the interactions between robots and deformable terrains that consist of granular material. Granular flows and their interactions with rigid bodies still pose several open questions. A promising direction for accurate, yet efficient, modeling is using continuum methods. Also, a new direction for real-time physics modeling is the use of deep learning. This research advances machine learning methods for modeling rigid body-driven granular flows, for application to terrestrial industrial machines as well as space robotics (where the effect of gra…
In this paper we present an early prototype of the Digger Finger that is designed to easily penetrate granular media and is equipped with the GelSight sensor. Identifying objects buried in granular media using tactile sensors is a challenging task. First, particle jamming in granular media prevents downward movement. Second, the granular media particles tend to get stuck between the sensing surface and the object of interest, distorting the actual shape of the object. To tackle these challenges we present a Digger Finger prototype. It is capable of fluidizing granular media during penetration …
From construction materials, such as sand or asphalt, to kitchen ingredients, like rice, sugar, or salt; the world is full of granular materials. Despite impressive progress in robotic manipulation, manipulating and interacting with granular material remains a challenge due to difficulties in perceiving, representing, modelling, and planning for these variable materials that have complex internal dynamics. While some prior work has looked into estimating or learning accurate dynamics models for granular materials, the literature is still missing a more abstract planning method that can be used…
Interactive Shaping of Granular Media Using Reinforcement Learning
Humanoid Robots Lab, University of Bonn, Germany（波恩大学）；Autonomous Intelligent Systems Lab, University of Bonn, Germany（波恩大学）；Center for Robotics, University of Bonn, Germany（波恩大学）；Lamarr Institute for ML and AI, Bonn, Germany（波恩人工智能与机器学习研究所）
Bipedal-Walking-Dynamics Model on Granular Terrains
Department of Mechanical and Aerospace Engineering, Rutgers University（罗格斯大学机械与航空航天工程系）；Bridgewater Raritan High School（布里奇沃特-拉里坦高中）；State Key Lab of Fluid Power Transmission and Control and the School of Mechanical Engineering, Zhejiang University（浙江大学流体动力传动与控制国家重点实验室和机械工程学院）
Interactive Identification of Granular Materials using Force Measurements
Intelligent Robotics Group at the Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, Finland（阿尔托大学电气工程与自动化系智能机器人组）；Section of Mechanical Technology at the Department of Engineering Technology and Didactics, Technical University of Denmark, Denmark（技术大学丹麦分校工程技术与教学系机械技术组）
The picking of one or more objects from an unsorted pile continues to be non-trivial for robotic systems. This is especially so when the pile consists of a granular material (GM) containing individual items that tangle with one another, causing more to be picked out than desired. One of the key features of such tangle-prone GMs is the presence of protrusions extending out from the main body of items in the pile. This work characterises the role the latter play in causing mechanical entanglement and their impact on picking consistency. It reports experiments in which picking GMs with different …
Robotic Perception with a Large Tactile-Vision-Language Model for Physical Property Inference
College of Big Data and Internet, Shenzhen Technology University, China（大数据与互联网学院，深圳科技大学，中国）；Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, China（中德智能制造学院，深圳科技大学，中国）；Robotics Department, Institute of Smart Systems and Artificial Intelligence (ISSAI), Nazarbayev University, Kazakhstan（机器人系，智能系统与人工智能研究所（ISSAI），纳扎尔巴耶夫大学，哈萨克斯坦）；Foundation Robotics Labs, Germany（基础机器人实验室，德国）
