Technical session talks from ICRA 2012
TechTalks from event: Technical session talks from ICRA 2012
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Hand Modeling and Control
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Reduced Dimensionality Control for the ACT HandRedundant tendon-driven systems such as the human hand or the ACT robotic hand are high-dimensional and nonlinear systems that make traditional control strategies ineffective. The synergy hypothesis from neuroscience suggests that employing dimensionality reduction techniques can simplify the system without a major loss in function. We define a dimensionality reduction framework consisting of separate observation and activation synergies, a first-order model, and an optimal controller. The framework is implemented for two example tasks: adaptive control of thumb posture and hybrid position/force control to enable dynamic handwriting.
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A Functional Anatomy Based Kinematic Human Hand Model with Simple Size AdaptationFor the purpose of ergonomic human-machine interaction and geometrical design of hand held haptic devices, a kinematic model that represents the functional anatomy of different human hands is desired. It is the goal of this paper to present a kinematic hand model that is based on human physiology and that is easily adaptable to represent various real human hand sizes. This is achieved by exploiting body proportions to derive finger segment lengths from the hand length. A partial hand model validation, involving index- and middle finger validation using a group of subjects, indicates that the use of body proportions offers a good estimate of finger length from a given hand length. Model estimated fingertip positions over a motion trajectory remain within reasonable limits when compared with experimental data for this subject group. The model is promising for usage in practical situations since only hand length, which is easy to measure or to obtain from literature, is required as an input. Phalange lengths, which are sparsely available from literature and difficult to measure, are generated by the model.
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Balancing Anatomy and Function in a Musculoskeletal Model of HandsMusculoskeletal models are effective tools for understanding living systems. To ensure proper model function, they must be checked against the literature or specimens. Existing checking methods require cadaver experimentation, highly knowledgeable medical personnel, and/or significant time. In this paper, we propose a quick and efficient method, called functional consistency checking, for use when these resources are not available. This method uses the literature to define a set of mathematical constraints, custom inverse dynamics software to interact with the model and its Jacobian in realtime and then evaluates the models consistency with these constraints. The method's usefulness will be demonstrated by constructing a human hand prototype, performing functional consistency checking, and then comparing the original to the output using data from a pianist motion capture.
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Grasping by Caging: A Promising Tool to Deal with UncertaintyThis paper presents a novel approach to deal with uncertainty in grasping. The basic idea is to initiate a caging manipulation state and then shrink fingers into immobilization to perform a practical grasping. Thanks to flexibility from caging, this procedure is intrinsically safe and gains tolerance towards uncertainty. Besides, we demonstrate that the minimum caging is immobilization and consequently propose using three or four fingers to manipulate planar convex objects in a grasping-by-caging way. Experimental results with physical simulation show the robustness and efficacy of our approach. We expect its leading benefits in saving finger number, conquering low-friction materials and especially, dealing with pose/shape uncertainty.
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Caging-Based Grasping by a Robot Hand with Rigid and Soft PartsCaging is a method to make an object inescapable from a closed region by rigid bodies. Position-controlled robot hands can capture an object and manipulate it via caging without force sensing or force control. However, the object in caging is freely movable in the closed region, which may not be allowed in some applications. In such cases, grasping is required. In this paper, we propose a new simple approach to grasping by position-controlled robot hands with the advantage of caging: caging-based grasping by a robot hand with rigid and soft parts. In caging-based grasping, we cage an object with the rigid parts of the hand, and construct a complete grasp with the soft parts. We formulate the caging-based grasping, and derive concrete conditions for caging-based grasping in planar and spatial cases, and show some experimental results.
- All Sessions
- 3D Surface Models, Point Cloud Processing
- Needle Steering
- Networked Robots
- Grasping and Manipulation
- Motion Planning II
- Estimation and Control for UAVs
- Multi Robots: Task Allocation
- Localization
- Perception for Autonomous Vehicles
- Rehabilitation Robotics
- Modular Robots & Multi-Agent Systems
- Mechanism Design of Mobile Robots
- Bipedal Robot Control
- Navigation and Visual Sensing
- Autonomy and Vision for UAVs
- RGB-D Localization and Mapping
- Micro and Nano Robots II
- Embodied Intelligence - Complient Actuators
- Grasping: Modeling, Analysis and Planning
- Learning and Adaptive Control of Robotic Systems I
- Marine Robotics I
- Animation & Simulation
- Planning and Navigation of Biped Walking
- Sensing for manipulation
- Sampling-Based Motion Planning
- Minimally Invasive Interventions II
- Biologically Inspired Robotics II
- Underactuated Robots
- Semiconductor Manufacturing
- Haptics
- Learning and Adaptation Control of Robotic Systems II
- Parts Handling and Manipulation
- Space Robotics
- Stochastic in Robotics and Biological Systems
- Path Planning and Navigation
- Biomimetics
- Micro - Nanoscale Automation
- Multi-Legged Robots
- Localization II
- Results of ICRA 2011 Robot Challenge
- Teleoperation
- Applied Machine Learning
- Hand Modeling and Control
- Multi-Robot Systems 1
- Medical Robotics I
- Micro/Nanoscale Automation II
- Visual Learning
- Continuum Robots
- Robust and Adaptive Control of Robotic Systems
- High Level Robot Behaviors
- Biologically Inspired Robotics
- Novel Robot Designs
- Compliance Devices and Control
- Video Session
- AI Reasoning Methods
- Redundant robots
- Localization and Mapping
- Climbing Robots
- Embodied Inteligence - iCUB
- Underactuated Grasping
- Data Based Learning
- Range Imaging
- Collision
- Industrial Robotics
- Human Detection and Tracking
- Trajectory Planning and Generation
- Stochastic Motion Planning
- Medical Robotics II
- Vision-Based Attention and Interaction
- Control and Planning for UAVs
- Embodied Soft Robots
- Mapping
- SLAM I
- Image-Guided Interventions
- Novel Actuation Technologies
- Micro/Nanoscale Automation III
- Human Like Biped Locamotion
- Marine Robotics II
- Force & Tactile Sensors
- Motion Path Planning I
- Mobile Manipulation: Planning & Control
- Simulation and Search in Grasping
- Control of UAVs
- Grasp Planning
- Humanoid Motion Planning and Control
- Surveillance
- Environment Mapping
- Octopus-Inspired Robotics
- Soft Tissue Interaction
- Pose Estimation
- Cable-Driven Mechanisms
- Parallel Robots
- SLAM II
- Intelligent Manipulation Grasping
- Formal Methods
- Sensor Networks
- Force, Torque and Contacts in Grasping and Assembly
- Hybrid Legged Robots
- Visual Tracking
- Physical Human-Robot Interaction
- Robotic Software, Programming Environments, and Frameworks
- Minimally invasive interventions I
- Multi-Robot Systems II
- Grasping: Learning and Estimation
- Non-Holonomic Motion Planning
- Calibration and Identification
- Compliant Nanopositioning
- Micro and Nano Robots I