Constraining contacts to remain fixed on an object during manipulation limits the potential workspace size, as motion is subject to the hand's kinematic topology. Finger gaiting is one way to alleviate such restraints. It allows contacts to be freely broken and remade so as to operate on different manipulation manifolds. This capability, however, has traditionally been difficult or impossible to practically realize. A finger gaiting system must simultaneously plan for and control forces on the object while maintaining stability during contact switching. This work alleviates the traditional requirement by taking advantage of system compliance, allowing the hand to more easily switch contacts while maintaining a stable grasp. Our method achieves complete SO(3) finger gaiting control of grasped objects against gravity by developing a manipulation planner that operates via orthogonal safe modes of a compliant, underactuated hand absent of tactile sensors or joint encoders. During manipulation, a low-latency 6D pose object tracker provides feedback via vision, allowing the planner to update its plan online so as to adaptively recover from trajectory deviations. The efficacy of this method is showcased by manipulating both convex and non-convex objects on a real robot. Its robustness is evaluated via perturbation rejection and long trajectory goals. To the best of the authors' knowledge, this is the first work that has autonomously achieved full SO(3) control of objects within-hand via finger gaiting and without a support surface, elucidating a valuable step towards realizing true robot in-hand manipulation capabilities.
Finger-gaiting manipulation is an important skill to achieve large-angle in-hand re-orientation of objects. However, achieving these gaits with arbitrary orientations of the hand is challenging due to the unstable nature of the task. In this work, we use model-free reinforcement learning (RL) to learn finger-gaiting only via precision grasps and demonstrate finger-gaiting for rotation about an axis purely using on-board proprioceptive and tactile feedback. To tackle the inherent instability of precision grasping, we propose the use of initial state distributions that enable effective explorati…
Technische Universität Darmstadt（达姆施塔特工业大学）；Max-Planck-Institut für Intelligente Systeme（马克斯·普朗克智能系统研究所）
Currently grip control during in-hand manipulation is usually modeled as part of a monolithic task, yielding complex controllers based on force control specialized for their situations. Such non-modular and specialized control approaches render the generalization of these controllers to new in-hand manipulation tasks difficult. Clearly, a grip control approach that generalizes well between several tasks would be preferable. We propose a modular approach where each finger is controlled by an independent tactile grip controller. Using signals from the human-inspired biotac sensor, we can predict…
Dexterous manipulation has broad applications in assembly lines, warehouses and agriculture. To perform large-scale manipulation tasks for various objects, a multi-fingered robotic hand sometimes has to sequentially adjust its grasping gestures, i.e. the finger gaits, to address the workspace limits and guarantee the object stability. However, realizing finger gaits planning in dexterous manipulation is challenging due to the complicated grasp quality metrics, uncertainties on object shapes and dynamics (mass and moment of inertia), and unexpected slippage under uncertain contact dynamics. In …
Design and Validation of an Under-actuated Robotic Finger with Synchronous Tendon Routing
ShanghaiTech University（上海科技大学）；School of Information Science and Technology（信息科学与技术学院）；ShanghaiTech Automation and Robotics (STAR) Center（上海科技大学自动化与机器人中心）；King’s College London（伦敦大学国王学院）
University of Utah Robotics Center（犹他大学机器人中心）；School of Computing University of Utah（犹他大学计算机学院）
This paper explores the problem of autonomous, in-hand regrasping--the problem of moving from an initial grasp on an object to a desired grasp using the dexterity of a robot's fingers. We propose a planner for this problem which alternates between finger gaiting, and in-grasp manipulation. Finger gaiting enables the robot to move a single finger to a new contact location on the object, while the remaining fingers stably hold the object. In-grasp manipulation moves the object to a new pose relative to the robot's palm, while maintaining the contact locations between the hand and object. Given t…
While compliant grippers have become increasingly commonplace in robot manipulation, finding the right stiffness and geometry for grasping the widest variety of objects remains a key challenge. Adjusting both stiffness and gripper geometry on the fly may provide the versatility needed to manipulate the large range of objects found in domestic environments. We present a system for actively controlling the geometry (inflation level) and compliance of Soft-bubble grippers - air filled, highly compliant parallel gripper fingers incorporating visuotactile sensing. The proposed system enables large,…
We use reinforcement learning (RL) to learn dexterous in-hand manipulation policies which can perform vision-based object reorientation on a physical Shadow Dexterous Hand. The training is performed in a simulated environment in which we randomize many of the physical properties of the system like friction coefficients and an object's appearance. Our policies transfer to the physical robot despite being trained entirely in simulation. Our method does not rely on any human demonstrations, but many behaviors found in human manipulation emerge naturally, including finger gaiting, multi-finger coo…
ARTiS: An Adaptive Robotic Gripper for Enhanced Tool Manipulation in Disassembly Applications
National Institute of Advanced Industrial Science and Technology (AIST)（日本国立先进工业科学技术研究所（AIST））；The University of Osaka（大阪大学）；Graduate School of Engineering Science, The University of Osaka（大阪大学工程科学研究生院）
We present the RBO Hand 3, a highly capable and versatile anthropomorphic soft hand based on pneumatic actuation. The RBO Hand 3 is designed to enable dexterous manipulation, to facilitate transfer of insights about human dexterity, and to serve as a robust research platform for extensive real-world experiments. It achieves these design goals by combining many degrees of actuation with intrinsic compliance, replicating relevant functioning of the human hand, and by combining robust components in a modular design. The RBO Hand 3 possesses 16 independent degrees of actuation, implemented in a de…
Hunan University（湖南大学）；Bielefeld University（比勒费尔德大学）；University of the West of England（西英格兰大学）
In this work, we focus on improving the robot's dexterous capability by exploiting visual sensing and adaptive force control. TeachNet, a vision-based teleoperation learning framework, is exploited to map human hand postures to a multi-fingered robot hand. We augment TeachNet, which is originally based on an imprecise kinematic mapping and position-only servoing, with a biomimetic learning-based compliance control algorithm for dexterous manipulation tasks. This compliance controller takes the mapped robotic joint angles from TeachNet as the desired goal, computes the desired joint torques. It…
Dorabot, Inc.（多机器人公司）；Northwestern University（西北大学）；Northwestern Institute on Complex Systems (NICO)（西北复杂系统研究所（NICO））
We investigate in-hand regrasping by pushing an object against an external constraint and allowing sliding at the fingertips. Each fingertip is modeled as attached to a multidimensional spring mounted to a position-controlled anchor. Spring compliance maps contact forces to spring compressions, ensuring the fingers remain in contact, and sliding "compliance" governs the relationship between sliding motions and tangential contact forces. A spring-sliding compliant regrasp is achieved by controlling the finger anchor motions. We derive the fingertip sliding mechanics for multifingered sliding re…
The synthesis of tactile sensing with compliance is essential to many fields, from agricultural usages like fruit picking, to sustainability practices such as sorting recycling, to the creation of safe home-care robots for the elderly to age with dignity. From tactile sensing, we can discern material properties, recognize textures, and determine softness, while with compliance, we are able to securely and safely interact with the objects and the environment around us. These two abilities can culminate into a useful soft robotic gripper, such as the original GelSight Fin Ray, which is able to g…
National University of Singapore（新加坡国立大学）；Nanyang Technological University（南洋理工大学）
Grasping is an essential capability for most robots in practical applications. Soft robotic grippers are considered as a critical part of robotic grasping and have attracted considerable attention in terms of the advantages of the high compliance and robustness to variance in object geometry; however, they are still limited by the corresponding sensing capabilities and actuation mechanisms. We propose a novel soft gripper that looks like a granary with a compliant snap-through bistable mechanism fabricated by integrated mold technology, achieving sensing and actuation purely mechanically. In p…
Compared to traditional rigid robotics, soft robotics has attracted increasing attention due to its advantages as compliance, safety, and low cost. As an essential part of soft robotics, the soft robotic gripper also shows its superior while grasping the objects with irregular shapes. Recent research has been conducted to improve its grasping performance by adjusting the variable effective length (VEL). However, the VEL achieved by multi-chamber design or tunable stiffness shape memory material requires complex pneumatic circuit design or a time-consuming phase-changing process. This work prop…
Amirkabir University of Technology (Tehran Polytechnic)（阿米尔卡比尔理工大学（德黑兰理工学院））
Soft grippers, for stable grasping of objects, with high compliance could be considered a suitable candidate for replacement of conventional rigid grippers, and in recent years, they have been emerging exponentially in industries. Not only are these highly adaptable grippers capable of static grasping of an object, but also they can be utilized for performing object manipulation tasks. Plenty of contemporary studies have been emphasizing on static grasping ability of soft grippers. However, in this thesis, planar in-hand object manipulation in a soft gripper which comprises a pair of soft fing…
Visuotactile Sensor Enabled Pneumatic Device Towards Compliant Oropharyngeal Swab Sampling
Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Seoul National University（首尔国立大学）；Qianyuan Institute of Science（乾元科学研究院）；The Seventh Affiliated Hospital, Sun Yat-sen University（中山大学附属第七医院）；Ryerson University（瑞尔森大学）
Soft optical tactile sensors enable robots to manipulate deformable objects by capturing important features such as high-resolution contact geometry and estimations of object compliance. This work presents a variable stiffness soft tactile end-effector called StRETcH, a Soft to Resistive Elastic Tactile Hand, that is easily manufactured and integrated with a robotic arm. An elastic membrane is suspended between two robotic fingers, and a depth sensor capturing the deformations of the elastic membrane enables sub-millimeter accurate estimates of contact geometries. The parallel-jaw gripper vari…
Many order fulfillment applications in logistics, such as packing, involve picking objects from unstructured piles before tightly arranging them in bins or shipping containers. Desirable robotic solutions in this space need to be low-cost, robust, easily deployable and simple to control. The current work proposes a complete pipeline for solving packing tasks for cuboid objects, given access only to RGB-D data and a single robot arm with a vacuum-based end-effector, which is also used as a pushing or dragging finger. The pipeline integrates perception for detecting the objects and planning so a…
