cytonGrasp: Cyton Alpha Controller via GraspIt! Simulation

This thesis addresses an expansion of the control programs for the Cyton Alpha 7D 1G arm. The original control system made use of configurable software which exploited the arm’s seven degrees of freedom and kinematic redundancy to control the arm based on desired behaviors that were configured off-line. The inclusions of the GraspIt! grasp planning simulator and toolkit enables the Cyton Alpha to be used in more proactive on-line grasping problems, as well as, presenting many additional tools for on-line learning applications. In short, GraspIt! expands what is possible with the Cyton Alpha to include many machine learning tools and opportunities for future research. Noteworthy features of GraspIt!: • A 3D user interface allowing the user to see and interact virtual objects, obstacles, and robots, in addition to a 3D representation of the Cyton Alpha • A collision detection and contact determination system within simulation • On-line grasp analysis routines • Visualization methods for determining the weak points within a grasp, as well as, creating projections of grasp quality and ability to resist dynamic forces. • Computation of numerical grasp quality metrics and visualization methods for proposed grasps • Dynamics engine • Support for lower-dimensional hand posture subspaces • Interaction with sensors (Flock of Birds tracker) and hardware (Pioneer robot) within simulation • GraspIt! can generate huge databases of labeled grasp data, which can be used for data-driven grasp-planning algorithms and has built in support for the Columbia Grasp Database. By making use of the GraspIt! simulator, it is possible to test algorithms for grasp manipulation, grasp planning, or grasp synthesis more quickly and with greater repeatability than would be possible on the real robot. Contributions of this system include: 1. A joint based 3D rendering of the Cyton Alpha 7D 1G arm 2. Simulated bodies for several objects in the DI Lab 3. Support for multiple representations of joint data within three-dimensional space • Euler Angles • Quaternions • Denavit-Hartenberg Parameters 4. Framework for future work in grasp-planning, grasp synthesis, cooperative grasping tasks, and transfer learning applications with the Cyton Alpha arm

Data-Driven Grasp Synthesis - A Survey

We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic

Optimization Model for Planning Precision Grasps with Multi-Fingered Hands

Precision grasps with multi-fingered hands are important for precise placement and in-hand manipulation tasks. Searching precision grasps on the object represented by point cloud, is challenging due to the complex object shape, high-dimensionality, collision and undesired properties of the sensing and positioning. This paper proposes an optimization model to search for precision grasps with multi-fingered hands. The model takes noisy point cloud of the object as input and optimizes the grasp quality by iteratively searching for the palm pose and finger joints positions. The collision between the hand and the object is approximated and penalized by a series of least-squares. The collision approximation is able to handle the point cloud representation of the objects with complex shapes. The proposed optimization model is able to locate collision-free optimal precision grasps efficiently. The average computation time is 0.50 sec/grasp. The searching is robust to the incompleteness and noise of the point cloud. The effectiveness of the algorithm is demonstrated by experiments.Comment: Submitted to IROS2019, experiment on BarrettHand, 8 page