Emergent Hand Morphology and Control from Optimizing Robust Grasps of Diverse Objects
NVIDIA（英伟达）；University of California, Berkeley（加州大学伯克利分校）；University of Toronto（多伦多大学）；Vector Institute（向量研究所）；Caltech（加州理工学院）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）
Evolution in nature illustrates that the creatures' biological structure and their sensorimotor skills adapt to the environmental changes for survival. Likewise, the ability to morph and acquire new skills can facilitate an embodied agent to solve tasks of varying complexities. In this work, we introduce a data-driven approach where effective hand designs naturally emerge for the purpose of grasping diverse objects. Jointly optimizing morphology and control imposes computational challenges since it requires constant evaluation of a black-box function that measures the performance of a combination of embodiment and behavior. We develop a novel Bayesian Optimization algorithm that efficiently co-designs the morphology and grasping skills jointly through learned latent-space representations. We design the grasping tasks based on a taxonomy of three human grasp types: power grasp, pinch grasp, and lateral grasp. Through experimentation and comparative study, we demonstrate the effectiveness of our approach in discovering robust and cost-efficient hand morphologies for grasping novel objects.
MachaGrasp: Morphology-Aware Cross-Embodiment Dexterous Hand Articulation Generation for Grasping
Robotics & Autonomous Systems Division, Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR-I 2 R)（机器人与自主系统 division，信息通信研究所，科技研究局）；College of Computing and Data Science, Nanyang Technological University（计算与数据科学学院，南洋理工大学）；Show Lab, National University of Singapore（Show Lab，国立新加坡大学）
A simple gripper can solve more complex manipulation tasks if it can utilize the external environment such as pushing the object against the table or a vertical wall, known as "Extrinsic Dexterity." Previous work in extrinsic dexterity usually has careful assumptions about contacts which impose restrictions on robot design, robot motions, and the variations of the physical parameters. In this work, we develop a system based on reinforcement learning (RL) to address these limitations. We study the task of "Occluded Grasping" which aims to grasp the object in configurations that are initially oc…
DiffMorphGrasp: A Diffusion Framework Conditioned on Hand Morphology for Cross-Embodiment Grasp Generation
Department of Engineering, King’s College London, Strand, London, WC2R 2LS, United Kingdom（伦敦国王学院工程系）；Imperial College London, London, SW7 2AZ, United Kingdom（伦敦帝国学院）
Department of Mechanical Engineering, Massachusetts Institute of Technology（麻省理工学院机械工程系）；Department of Mechanical Engineering, Stanford University（斯坦福大学机械工程系）
Single-grasp deformable object discrimination: the effect of gripper morphology, sensing modalities, and action parameters
Czech Technical University in Prague（布拉格捷克理工大学）；Faculty of Electrical Engineering, Czech Technical University in Prague（布拉格捷克理工大学电气工程学院）；Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague（布拉格捷克理工大学捷克信息学、机器人学与控制论研究所）
We present a novel method for the direct transfer of grasps and manipulations between objects and hands through utilization of contact areas. Our method fully preserves contact shapes, and in contrast to existing techniques, is not dependent on grasp families, requires no model training or grasp sampling, makes no assumptions about manipulator morphology or kinematics, and allows user control over both transfer parameters and solution optimization. Despite these accommodations, we show that our method is capable of synthesizing kinematically feasible whole hand poses in seconds even for poor i…
SynManDex: Synthesizing Human-like Dexterous Grasps from Synthetic Human Pre-Grasps
Shanghai AI Lab（上海人工智能实验室）；Shanghai Jiaotong University（上海交通大学）；Shenzhen University（深圳大学）；Fudan University（复旦大学）；University of Hong Kong（香港大学）；ZTE Corporation（中兴通讯股份有限公司）
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 …
Queensland University of Technology（昆士兰科技大学）；Australian Centre for Robotic Vision（澳大利亚机器人视觉中心）
We present the grasping system and design approach behind Cartman, the winning entrant in the 2017 Amazon Robotics Challenge. We investigate the design processes leading up to the final iteration of the system and describe the emergent solution by comparing it with key robotics design aspects. Following our experience, we propose a new design aspect, precision vs. redundancy, that should be considered alongside the previously proposed design aspects of modularity vs. integration, generality vs. assumptions, computation vs. embodiment and planning vs. feedback. We present the grasping system be…
Whole-Body Proprioceptive Morphing: A Modular Soft Gripper for Robust Cross-Scale Grasping
Mechanical Engineering Department at the University of Michigan-Ann Arbor（密歇根大学安娜堡分校机械工程系）；Robotics Department at the University of Michigan-Ann Arbor（密歇根大学安娜堡分校机器人学系）
Uni-PNP: Unified Prehensile and Non-prehensile Policy Learning for Generalizable Object Pose Reaching
Peking University（北京大学）；Galbot；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Tsinghua University（清华大学）
InterMASH: A Unified Geometric Representation for Grasp Synthesis
University of Science and Technology of China（中国科学技术大学）；Jiangsu Key Laboratory of AI for Industries and Institute of AI for Industries Chinese Academy of Sciences（江苏省人工智能产业重点实验室及中国科学院人工智能产业研究院）；Shenzhen University（深圳大学）
