The engineering design of robotic grippers presents an ample design space for optimization towards robust grasping. In this paper, we adopt the reconfigurable design of the robotic gripper using a novel soft finger structure with omni-directional adaptation, which generates a large number of possible gripper configurations by rearranging these fingers. Such reconfigurable design with these omni-adaptive fingers enables us to systematically investigate the optimal arrangement of the fingers towards robust grasping. Furthermore, we adopt a learning-based method as the baseline to benchmark the effectiveness of each design configuration. As a result, we found that a 3-finger and 4-finger radial configuration is the most effective one achieving an average 96\% grasp success rate on seen and novel objects selected from the YCB dataset. We also discussed the influence of the frictional surface on the finger to improve the grasp robustness.
Bio-inspired reconfigurable stereo vision for robotics using omnidirectional cameras
Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems, Southern University of Science and Technology（深圳智能机器人与柔性制造系统重点实验室，南方科技大学）
This letter proposes a novel single-fingered reconfigurable robotic gripper for grasping objects in narrow working spaces. The finger of the developed gripper realizes two configurations, namely, the insertion and grasping modes, using only a single motor. In the insertion mode, the finger assumes a thin shape such that it can insert its tip into a narrow space. The grasping mode of the finger is activated through a folding mechanism. Mode switching can be achieved in two ways: switching the mode actively by a motor, or combining passive rotation of the fingertip through contact with the suppo…
Beijing Institute for General Artificial Intelligence (BIGAI)（北京通用人工智能研究院）；Peking University（北京大学）；Tsinghua University（清华大学）
In this work, we present a reconfigurable data glove design to capture different modes of human hand-object interactions, which are critical in training embodied artificial intelligence (AI) agents for fine manipulation tasks. To achieve various downstream tasks with distinct features, our reconfigurable data glove operates in three modes sharing a unified backbone design that reconstructs hand gestures in real time. In the tactile-sensing mode, the glove system aggregates manipulation force via customized force sensors made from a soft and thin piezoresistive material; this design minimizes i…
A Novel Reconfigurable Dexterous Hand Based on Triple-Symmetric Bricard Parallel Mechanism
Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University（人工智能与机器人研究所，工程与技术学院，复旦大学）；Department of Mechanical Engineering, The Hong Kong Polytechnic University（机械工程系，香港理工大学）
University of Hong Kong（香港大学）；Southern University of Science and Technology（南方科技大学）；SUSTech Institute of Intelligent Manufacturing（南方科技大学智能制造研究院）
This paper presents a novel design of a soft tactile finger with omni-directional adaptation using multi-channel optical fibers for rigid-soft interactive grasping. Machine learning methods are used to train a model for real-time prediction of force, torque, and contact using the tactile data collected. We further integrated such fingers in a reconfigurable gripper design with three fingers so that the finger arrangement can be actively adjusted in real-time based on the tactile data collected during grasping, achieving the process of rigid-soft interactive grasping. Detailed sensor calibratio…
AllTact Fin Ray: A Compliant Robot Gripper with Omni-Directional Tactile Sensing
Department of Mechanical Engineering, Tsinghua University（清华大学机械工程系）；Tsinghua-Berkeley Shenzhen Institute（清华大学-伯克利深圳研究院）；Institute of Data and Information, Shenzhen International Graduate School, Tsinghua University（清华大学数据与信息研究所、深圳国际研究生院）
本文提出 AllTact Fin Ray 柔顺机器人夹爪，通过相机捕捉指体形变，并提出基于边缘特征重建全局形变与动态参考图像亮度差异计算接触几何的新方法，实现了全向触觉感知，在接触检测、力估计和精确操作等任务中验证了有效性。
Triplane Grasping: Efficient 6-DoF Grasping with Single RGB Images
Computer Science Department, Faculty of Science and Engineering, Swansea University（计算机科学系，科学与工程学院，斯旺西大学）；Department of Computer Science, Durham University（计算机科学系，达勒姆大学）
Southern University of Science and Technology（南方科技大学）；University of Hong Kong（香港大学）
Over the past few decades, efforts have been made towards robust robotic grasping, and therefore dexterous manipulation. The soft gripper has shown their potential in robust grasping due to their inherent properties-low, control complexity, and high adaptability. However, the deformation of the soft gripper when interacting with objects bring inaccuracy of grasped objects, which causes instability for robust grasping and further manipulation. In this paper, we present an omni-directional adaptive soft finger that can sense deformation based on embedded optical fibers and the application of mac…
A novel quadcopter capable of changing shape mid-flight is presented, allowing for operation in four configurations with the capability of sustained hover in three. This is accomplished without requiring actuators beyond the four motors typical of a quadcopter. Morphing is achieved through freely-rotating hinges that allow the vehicle arms to fold downwards by either reducing or reversing thrust forces. Constraints placed on the control inputs of the vehicle prevent the arms from folding or unfolding unexpectedly. This allows for the use of existing quadcopter controllers and trajectory genera…
University of California San Diego（加州大学圣地亚哥分校）；NASA Jet Propulsion Laboratory（美国国家航空航天局喷气推进实验室）
This paper presents the design and performance of a screw-propelled redundant serpentine robot. This robot comprises serially linked, identical modules, each incorporating an Archimedes' screw for propulsion and a universal joint (U-Joint) for orientation control. When serially chained, these modules form a versatile snake robot platform which enables the robot to reshape its body configuration for varying environments and gait patterns that would be typical of snake movement. Furthermore, the Archimedes' screws allow for novel omni-wheel drive-like motions by speed controlling their screw thr…
SUSTech Institute of Robotics（南方科技大学机器人研究院）；Southern University of Science and Technology（南方科技大学）
We present DeepClaw as a reconfigurable benchmark of robotic hardware and task hierarchy for robot learning. The DeepClaw benchmark aims at a mechatronics perspective of the robot learning problem, which features a minimum design of robot cell that can be easily reconfigured to host robot hardware from various vendors, including manipulators, grippers, cameras, desks, and objects, aiming at a streamlined collection of physical manipulation data and evaluation of the learned skills for hardware benchmarking. We provide a detailed design of the robot cell with readily available parts to build th…
