239 research outputs found
Examples of 3D grasp quality computations
Previous grasp quality research is mainly theoretical, and has assumed that contact types and positions are given, in order to preserve the generality of the proposed quality measures. The example results provided by these works either ignore hand geometry and kinematics entirely or involve only the simplest of grippers. We present a unique grasp analysis system that, when given a 3D object, hand, and pose for the hand, can accurately determine the types of contacts that will occur between the links of the hand and the object, and compute two measures of quality for the grasp. Using models of two articulated robotic hands, we analyze several grasps of a polyhedral model of a telephone handset, and we use a novel technique to visualize the 6D space used in these computations. In addition, we demonstrate the possibility of using this system for synthesizing high quality grasps by performing a search over a subset of possible hand configurations
Prioritized independent contact regions for form closure grasps
Proceedings of: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'11), September 25-30, 2011, San Francisco, USAThe concept of independent contact regions on a
target object’s surface, in order to compensate for shortcomings
in the positioning accuracy of robotic grasping devices, is well
known. However, the numbers and distributions of contact
points forming such regions is not unique and depends on the
underlying computational method. In this work we present a
computation scheme allowing to prioritize contact points for
inclusion in the independent regions. This enables a user to
affect their shape in order to meet the demands of the targeted
application. The introduced method utilizes frictionless contact constraints and is able to efficiently approximate the space of disturbances resistible by all grasps comprising contacts within the independent regions.European Community's Seventh Framework ProgramThis research has been partially supported by the HANDLE project, funded by the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement ICT 231640
Instance-wise Grasp Synthesis for Robotic Grasping
Generating high-quality instance-wise grasp configurations provides critical
information of how to grasp specific objects in a multi-object environment and
is of high importance for robot manipulation tasks. This work proposed a novel
\textbf{S}ingle-\textbf{S}tage \textbf{G}rasp (SSG) synthesis network, which
performs high-quality instance-wise grasp synthesis in a single stage: instance
mask and grasp configurations are generated for each object simultaneously. Our
method outperforms state-of-the-art on robotic grasp prediction based on the
OCID-Grasp dataset, and performs competitively on the JACQUARD dataset. The
benchmarking results showed significant improvements compared to the baseline
on the accuracy of generated grasp configurations. The performance of the
proposed method has been validated through both extensive simulations and real
robot experiments for three tasks including single object pick-and-place, grasp
synthesis in cluttered environments and table cleaning task
A Two-Stage Learning Architecture That Generates High-Quality Grasps for a Multi-Fingered Hand
In this work, we investigate the problem of planning stable grasps for object manipulations using an 18-DOF robotic hand with four fingers. The main challenge here is the high-dimensional search space, and we address this problem using a novel two-stage learning process. In the first stage, we train an autoregressive network called the hand-pose-generator, which learns to generate a distribution of valid 6D poses of the palm for a given volumetric object representation. In the second stage, we employ a network that regresses 12D finger positions and scalar grasp qualities from given object representations and palm poses. To train our networks, we use synthetic training data generated by a novel grasp planning algorithm, which also proceeds stage-wise: first the palm pose, then the finger positions. Here, we devise a Bayesian Optimization scheme for the palm pose and a physics-based grasp pose metric to rate stable grasps. In experiments on the YCB benchmark data set, we show a grasp success rate of over 83%, as well as qualitative results on real scenarios of grasping unknown objects
Planning dextrous robot hand grasps from range data, using preshapes and digit trajectories
Dextrous robot hands have many degrees of freedom. This enables the manipulation of
objects between the digits of the dextrous hand but makes grasp planning substantially
more complex than for parallel jaw grippers. Much of the work that addresses grasp
planning for dextrous hands concentrates on the selection of contact sites to optimise
stability criteria and ignores the kinematics of the hand. In more complete systems,
the paradigm of preshaping has emerged as dominant. However, the criteria for the
formation and placement of the preshapes have not been adequately examined, and
the usefulness of the systems is therefore limited to grasping simple objects for which
preshapes can be formed using coarse heuristics.In this thesis a grasp metric based on stability and kinematic feasibility is introduced.
The preshaping paradigm is extended to include consideration of the trajectories that
the digits take during closure from preshape to final grasp. The resulting grasp family
is dependent upon task requirements and is designed for a set of "ideal" object-hand
configurations. The grasp family couples the degrees of freedom of the dextrous hand
in an anthropomorphic manner; the resulting reduction in freedom makes the grasp
planning less complex. Grasp families are fitted to real objects by optimisation of the
grasp metric; this corresponds to fitting the real object-hand configuration as close to
the ideal as possible. First, the preshape aperture, which defines the positions of the
fingertips in the preshape, is found by optimisation of an approximation to the grasp
metric (which makes simplifying assumptions about the digit trajectories and hand
kinematics). Second, the full preshape kinematics and digit closure trajectories are
calculated to optimise the full grasp metric.Grasps are planned on object models built from laser striper range data from two
viewpoints. A surface description of the object is used to prune the space of possible
contact sites and to allow the accurate estimation of normals, which is required by the
grasp metric to estimate the amount of friction required. A voxel description, built by
ray-casting, is used to check for collisions between the object and the robot hand using
an approximation to the Euclidean distance transform.Results are shown in simulation for a 3-digit hand model, designed to be like a simplified
human hand in terms of its size and functionality. There are clear extensions of the
method to any dextrous hand with a single thumb opposing multiple fingers and several
different hand models that could be used are described. Grasps are planned on a wide
variety of curved and polyhedral object
A Continuous Grasp Representation for the Imitation Learning of Grasps on Humanoid Robots
Models and methods are presented which enable a humanoid robot to learn reusable, adaptive grasping skills. Mechanisms and principles in human grasp behavior are studied. The findings are used to develop a grasp representation capable of retaining specific motion characteristics and of adapting to different objects and tasks. Based on the representation a framework is proposed which enables the robot to observe human grasping, learn grasp representations, and infer executable grasping actions
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Quantifying grasp quality using an inverse reinforcement learning algorithm
This thesis considers the problem of using a learning algorithm to recognize when a mechanical gripper and sensor combination has achieved a robust grasp. Robotic hands are continuously evolving with finer motor control and higher degrees of freedom which can complicate the ability of an operator to determine if a gripper has achieved a successful grasp. Robots working in hazardous environments especially need confirmation of a successful grasp as the cost of failure is often higher than in traditional factory environments. The object set found in a nuclear environment is the focus of this effort. Objects in this environment are typically expensive (or one-of-a-kind), rigid, radioactive (or toxic), dense, and susceptible to dents, scratches, and oxidation. To validate the robustness of a grasp option, an online inverse reinforcement learning approach is evaluated as a method to quantify grasp quality. This approach is applied to an industrial-grade under-actuated robotic hand equipped with 36 pressure sensors. An expert trains the inverse reinforcement learning algorithm to generate a reward function which scores each grasp so - when combined with fuzzy logic - provides a general success or fail along with a confidence level. Utilizing the trained inverse reinforcement learning algorithm in a glovebox environment reduces the number of potential failing and untrustworthy grasps by scoring executed grasps and rejecting grasps that are similar to prior failed grasps while allowing further execution of movement when a grasp has been scored highly. The trained algorithm incorrectly classified grasps of insufficient quality less than 5% of the time in experimental hardware tests, showing that the algorithm can be applied to the glovebox environment to improve grasp safety. Thus the combination of grasp selection and pressure sensor validation provides a more efficient, robust, and redundant method to assure items can be safely handled during remote automation processes.Mechanical Engineerin
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