Data-driven approach for grasping shows significant advance recently. But these approaches usually require much training data. To increase the efficiency of grasping data collection, this paper presents a novel grasp training system including the whole pipeline from data collection to model inference. The system can collect effective grasp sample with a corrective strategy assisted by antipodal grasp rule, and we design an affordance interpreter network to predict pixelwise grasp affordance map. We define graspability, ungraspability and background as grasp affordances. The key advantage of our system is that the pixel-level affordance interpreter network trained with only a small number of grasp samples under antipodal rule can achieve significant performance on totally unseen objects and backgrounds. The training sample is only collected in simulation. Extensive qualitative and quantitative experiments demonstrate the accuracy and robustness of our proposed approach. In the real-world grasp experiments, we achieve a grasp success rate of 93% on a set of household items and 91% on a set of adversarial items with only about 6,300 simulated samples. We also achieve 87% accuracy in clutter scenario. Although the model is trained using only RGB image, when changing the background textures, it also performs well and can achieve even 94% accuracy on the set of adversarial objects, which outperforms current state-of-the-art methods.
RAGNet: Large-scale Reasoning-based Affordance Segmentation Benchmark towards General Grasping
The Chinese University of Hong Kong（香港中文大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Dexmal；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；SKL-IOTSC, CIS, University of Macau（澳门科学馆-物联网与智能系统研究中心，澳门大学）
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Facebook AI Research（Facebook人工智能研究院）
Dexterous robotic hands are appealing for their agility and human-like morphology, yet their high degree of freedom makes learning to manipulate challenging. We introduce an approach for learning dexterous grasping. Our key idea is to embed an object-centric visual affordance model within a deep reinforcement learning loop to learn grasping policies that favor the same object regions favored by people. Unlike traditional approaches that learn from human demonstration trajectories (e.g., hand joint sequences captured with a glove), the proposed prior is object-centric and image-based, allowing …
PreAfford: Universal Affordance-Based Pre-Grasping for Diverse Objects and Environments
Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院（AIR））；Xingjian College, Tsinghua University（清华大学行健书院）；CFCS, School of Computer Science, Peking University（北京大学计算机学院CFCS）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院）；School of Vehicle and Mobility, Tsinghua University（清华大学车辆与运载学院）
Princeton University（普林斯顿大学）；Massachusetts Institute of Technology（麻省理工学院）
This paper presents a robotic pick-and-place system that is capable of grasping and recognizing both known and novel objects in cluttered environments. The key new feature of the system is that it handles a wide range of object categories without needing any task-specific training data for novel objects. To achieve this, it first uses a category-agnostic affordance prediction algorithm to select and execute among four different grasping primitive behaviors. It then recognizes picked objects with a cross-domain image classification framework that matches observed images to product images. Since…
Multi-Keypoint Affordance Representation for Functional Dexterous Grasping
School of Artificial Intelligence and Robotics, Hunan University（人工智能与机器人学院，湖南大学）；College of Computer Science and Electronic Engineering, Hunan University（计算机科学与电子工程学院，湖南大学）；National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University（机器人视觉感知与控制技术国家工程研究中心，湖南大学）
School of CS & AI, Arizona State University（亚利桑那州立大学计算机科学与人工智能学院）
Conventional works that learn grasping affordance from demonstrations need to explicitly predict grasping configurations, such as gripper approaching angles or grasping preshapes. Classic motion planners could then sample trajectories by using such predicted configurations. In this work, our goal is instead to fill the gap between affordance discovery and affordance-based policy learning by integrating the two objectives in an end-to-end imitation learning framework based on deep neural networks. From a psychological perspective, there is a close association between attention and affordance. T…
Learning Granularity-Aware Affordances from Human-Object Interaction for Tool-Based Functional Dexterous Grasping
School of Artificial Intelligence and Robotics, Hunan University（人工智能与机器人学院，湖南大学）；National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University（机器人视觉感知与控制技术国家工程研究中心，湖南大学）
AffordGrasp: In-Context Affordance Reasoning for Open-Vocabulary Task-Oriented Grasping in Clutter
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Peking University（北京大学）
Tsinghua University（清华大学）；Shenzhen Academy of Robotics（深圳机器人研究院）
In this paper, a novel robotic grasping system is established to automatically pick up objects in cluttered scenes. A composite robotic hand composed of a suction cup and a gripper is designed for grasping the object stably. The suction cup is used for lifting the object from the clutter first and the gripper for grasping the object accordingly. We utilize the affordance map to provide pixel-wise lifting point candidates for the suction cup. To obtain a good affordance map, the active exploration mechanism is introduced to the system. An effective metric is designed to calculate the reward for…
Perception-for-grasping is a challenging problem in robotics. Inexpensive range sensors such as the Microsoft Kinect provide sensing capabilities that have given new life to the effort of developing robust and accurate perception methods for robot grasping. This paper proposes a new approach to localizing enveloping grasp affordances in 3-D point clouds efficiently. The approach is based on modeling enveloping grasp affordances as a cylindrical shells that corresponds to the geometry of the robot hand. A fast and accurate fitting method for quadratic surfaces is the core of our approach. An ev…
Artificial intelligence is essential to succeed in challenging activities that involve dynamic environments, such as object manipulation tasks in indoor scenes. Most of the state-of-the-art literature explores robotic grasping methods by focusing exclusively on attributes of the target object. When it comes to human perceptual learning approaches, these physical qualities are not only inferred from the object, but also from the characteristics of the surroundings. This work proposes a method that includes environmental context to reason on an object affordance to then deduce its grasping regio…
Learning 6-DoF Fine-grained Grasp Detection Based on Part Affordance Grounding
Center for X-Mechanics, School of Aeronautics and Astronautics, Zhejiang University（浙江大学航空航天学院X力学中心）；Information Hub, Data Science and Analytics Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）信息中心、数据科学与分析方向）；Advanced Manufacturing Lab, Huawei Technologies（华为技术先进制造实验室）；Research Institute, UBTECH Robotics Inc.（UBTECH机器人研究院）；School of Information and School of Smart Governance, Renmin University of China（中国人民大学信息学院和智能治理学院）；School of Engineering, Westlake University and the Institute of Advanced Technology, Westlake Institute for Advanced Study（西湖大学工程学院和西湖先进科技研究院）；Department of Electrical and Computer Engineering, Dalhousie University（达尔豪斯大学电气与计算机工程系）
This paper introduces a challenging object grasping task and proposes a self-supervised learning approach. The goal of the task is to grasp an object which is not feasible with a single parallel gripper, but only with harnessing environment fixtures (e.g., walls, furniture, heavy objects). This Slide-to-Wall grasping task assumes no prior knowledge except the partial observation of a target object. Hence the robot should learn an effective policy given a scene observation that may include the target object, environmental fixtures, and any other disturbing objects. We formulate the problem as v…
University of Edinburgh（爱丁堡大学）；Heriot-Watt University（赫瑞-瓦特大学）
Reasoning about object affordances allows an autonomous agent to perform generalised manipulation tasks among object instances. While current approaches to grasp affordance estimation are effective, they are limited to a single hypothesis. We present an approach for detection and extraction of multiple grasp affordances on an object via visual input. We define semantics as a combination of multiple attributes, which yields benefits in terms of generalisation for grasp affordance prediction. We use Markov Logic Networks to build a knowledge base graph representation to obtain a probability dist…
AffordGrasp: Cross-Modal Diffusion for Affordance-Aware Grasp Synthesis
ShanghaiTech University（上海科技大学）；Shanghai Engineering Research Center of Intelligent Vision and Imaging（上海智能视觉与成像工程技术研究中心）；University of Science and Technology of China（中国科学技术大学）
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Stanford University（斯坦福大学）
Grasp detection in clutter requires the robot to reason about the 3D scene from incomplete and noisy perception. In this work, we draw insight that 3D reconstruction and grasp learning are two intimately connected tasks, both of which require a fine-grained understanding of local geometry details. We thus propose to utilize the synergies between grasp affordance and 3D reconstruction through multi-task learning of a shared representation. Our model takes advantage of deep implicit functions, a continuous and memory-efficient representation, to enable differentiable training of both tasks. We t…
ScaleADFG: Affordance-based Dexterous Functional Grasping via Scalable Dataset
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；CasiaHand Robotics Co., Ltd（CasiaHand机器人有限公司）；SenseTime Research, SenseTime（商汤研究，商汤）；Idiap Research Institute（Idiap研究所）
University of Innsbruck（因斯布鲁克大学）；University of Trento（特伦托大学）
Affordance detection from visual input is a fundamental step in autonomous robotic manipulation. Existing solutions to the problem of affordance detection rely on convolutional neural networks. However, these networks do not consider the spatial arrangement of the input data and miss parts-to-whole relationships. Therefore, they fall short when confronted with novel, previously unseen object instances or new viewpoints. One solution to overcome such limitations can be to resort to capsule networks. In this paper, we introduce the first affordance detection network based on dynamic tree-structu…
DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation
TAMS (Technical Aspects of Multimodal Systems), Department of Informatics, Universität Hamburg（技术多模态系统（TAMS），信息学院，汉堡大学）；Agile Robots AG（敏捷机器人公司）；Technical University of Munich（慕尼黑技术大学）
University of Stuttgart（斯图加特大学）；Technical University of Berlin（柏林工业大学）
Motion prediction in unstructured environments is a difficult problem and is essential for safe and efficient human-robot space sharing and collaboration. In this work, we focus on manipulation movements in environments such as homes, workplaces or restaurants, where the overall task and environment can be leveraged to produce accurate motion prediction. For these cases we propose an algorithmic framework that accounts explicitly for the environment geometry based on a model of affordances and a model of short-term human dynamics both trained on motion capture data. We propose dedicated functi…
GraspFoM: Towards Reconstruction-Driven Robotic Grasping with 3D Foundation Priors
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Peking University（北京大学）；The Hong Kong University of Science and Technology（香港科技大学）
While grasps must satisfy the grasping stability criteria, good grasps depend on the specific manipulation scenario: the object, its properties and functionalities, as well as the task and grasp constraints. In this paper, we consider such information for robot grasping by leveraging manifolds and symbolic object parts. Specifically, we introduce a new probabilistic logic module to first semantically reason about pre-grasp configurations with respect to the intended tasks. Further, a mapping is learned from part-related visual features to good grasping points. The probabilistic logic module ma…
While many quality metrics exist to evaluate the quality of a grasp by itself, no clear quantification of the quality of a grasp relatively to the task the grasp is used for has been defined yet. In this paper we propose a framework to extend the concept of grasp quality metric to task-oriented grasping by defining affordance functions via basic grasp metrics for an open set of task affordances. We evaluate both the effectivity of the proposed task oriented metrics and their practical applicability by learning to infer them from vision. Indeed, we assess the validity of our novel framework bot…
Affordance-Guided Enveloping Grasp Demonstration Toward Non-destructive Disassembly of Pinch-Infeasible Mating Parts
Department of Systems Innovation, Graduate School of Engineering Science, The University of Osaka（大阪大学工学研究院系统创新部门）；Industrial Cyber-physical Systems Research Center, The National Institute of Advanced Industrial Science and Technology (AIST)（国家先进工业科学与技术研究院工业网络物理系统研究中心）
TD-TOG Dataset: Benchmarking Zero-Shot and One-Shot Task-Oriented Grasping for Object Generalization
University of Aberdeen（阿伯丁大学）；Bank of Valletta（瓦莱塔银行）；Universite Claude Bernard Lyon 1（里昂第一大学）；CNRS（法国国家科学研究中心）；École Centrale de Lyon（里昂中央理工学院）；INSA Lyon（里昂国立应用科学学院）；Université Lumière Lyon 2（里昂第二大学）；Technical University of Applied Sciences Würzburg-Schweinfurt（维尔茨堡-施韦因富特应用技术大学）
Universität Hamburg（汉堡大学）；Agile Robots AG（敏捷机器人公司）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Tsinghua University（清华大学）
Currently, task-oriented grasp detection approaches are mostly based on pixel-level affordance detection and semantic segmentation. These pixel-level approaches heavily rely on the accuracy of a 2D affordance mask, and the generated grasp candidates are restricted to a small workspace. To mitigate these limitations, we first construct a novel affordance-based grasp dataset and propose a 6-DoF task-oriented grasp detection framework, which takes the observed object point cloud as input and predicts diverse 6-DoF grasp poses for different tasks. Specifically, our implicit estimation network and …
AffordDexGrasp: Open-set Language-guided Dexterous Grasp with Generalizable-Instructive Affordance
School of Computer Science and Engineering, Sun Yat-sen University, China（中山大学计算机科学与工程学院）；Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China（教育部人工智能与高级计算重点实验室）
Google（谷歌）；Massachusetts Institute of Technology（麻省理工学院）；Columbia University（哥伦比亚大学）
Does having visual priors (e.g. the ability to detect objects) facilitate learning to perform vision-based manipulation (e.g. picking up objects)? We study this problem under the framework of transfer learning, where the model is first trained on a passive vision task, and adapted to perform an active manipulation task. We find that pre-training on vision tasks significantly improves generalization and sample efficiency for learning to manipulate objects. However, realizing these gains requires careful selection of which parts of the model to transfer. Our key insight is that outputs of standa…
Robotic grasping is one of the most fundamental robotic manipulation tasks and has been actively studied. However, how to quickly teach a robot to grasp a novel target object in clutter remains challenging. This paper attempts to tackle the challenge by leveraging object attributes that facilitate recognition, grasping, and quick adaptation. In this work, we introduce an end-to-end learning method of attribute-based robotic grasping with one-grasp adaptation capability. Our approach fuses the embeddings of a workspace image and a query text using a gated-attention mechanism and learns to predi…
Deep Learning Approaches to Grasp Synthesis: A Review
Monash University（莫纳什大学）；The Australian National University（澳大利亚国立大学）；NVIDIA Corporation（英伟达公司）；LYRO Robotics Pty Ltd（LYRO机器人私人有限公司）；Stanford University（斯坦福大学）；Jaume I University（海梅一世大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；KTH Royal Institute of Technology（KTH皇家理工学院）；University of Washington（华盛顿大学）；Deakin University（迪肯大学）
Purdue University（普渡大学）；Massachusetts Institute of Technology（麻省理工学院）
Cloth in the real world is often crumpled, self-occluded, or folded in on itself such that key regions, such as corners, are not directly graspable, making manipulation difficult. We propose a system that leverages visual and tactile perception to unfold the cloth via grasping and sliding on edges. By doing so, the robot is able to grasp two adjacent corners, enabling subsequent manipulation tasks like folding or hanging. As components of this system, we develop tactile perception networks that classify whether an edge is grasped and estimate the pose of the edge. We use the edge classificatio…
Robotic grasping traditionally relies on object features or shape information for learning new or applying already learned grasps. We argue however that such a strong reliance on object geometric information renders grasping and grasp learning a difficult task in the event of cluttered environments with high uncertainty where reasonable object models are not available. This being so, in this paper we thus investigate the application of model-free stochastic optimization for grasp learning. For this, our proposed learning method requires just a handful of user-demonstrated grasps and an initial…
