Environment models serve as important resources for an autonomous robot by providing it with the necessary task-relevant information about its habitat. Their use enables robots to perform their tasks more reliably, flexibly, and efficiently. As autonomous robotic platforms get more sophisticated manipulation capabilities, they also need more expressive and comprehensive environment models: for manipulation purposes their models have to include the objects present in the world, together with their position, form, and other aspects, as well as an interpretation of these objects with respect to the robot tasks. The dissertation presented in this article (Rusu, PhD thesis, 2009) proposes Semantic 3D Object Models as a novel representation of the robot’s operating environment that satisfies these requirements and shows how these models can be automatically acquired from dense 3D range data.

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Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments

The usefulness and versatility of a robotic end-effector depends on the diversity of grasps it can accomplish and also on the complexity of the control methods required to achieve them. We believe that soft hands are able to provide diverse and robust grasping with low control complexity. They possess many mechanical degrees of freedom and are able to implement complex deformations. At the same time, due to the inherent compliance of soft materials, only very few of these mechanical degrees have to be controlled explicitly. Soft hands therefore may combine the best of both worlds. In this paper, we present RBO Hand 2, a highly compliant, underactuated, robust, and dexterous anthropomorphic hand. The hand is inexpensive to manufacture and the morphology can easily be adapted to specific applications. To enable efficient hand design, we derive and evaluate computational models for the mechanical properties of the hand's basic building blocks, called PneuFlex actuators. The versatility of RBO Hand 2 is evaluated by implementing the comprehensive Feix taxonomy of human grasps. The manipulator's capabilities and limits are demonstrated using the Kapandji test and grasping experiments with a variety of objects of varying weight. Furthermore, we demonstrate that the effective dimensionality of grasp postures exceeds the dimensionality of the actuation signals, illustrating that complex grasping behavior can be achieved with relatively simple control.

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A novel type of compliant and underactuated robotic hand for dexterous grasping

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.

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Data-Driven Grasp Synthesis—A Survey

In this paper we present the Yale-CMU-Berkeley (YCB) Object and Model set, intended to be used for benchmarking in robotic grasping and manipulation research. The objects in the set are designed to cover various aspects of the manipulation problem; it includes objects of daily life with different shapes, sizes, textures, weight and rigidity, as well as some widely used manipulation tests. The associated database provides high-resolution RGBD scans, physical properties and geometric models of the objects for easy incorporation into manipulation and planning software platforms. A comprehensive literature survey on existing benchmarks and object datasets is also presented and their scope and limitations are discussed. The set will be freely distributed to research groups worldwide at a series of tutorials at robotics conferences, and will be otherwise available at a reasonable purchase cost.

/pdf/the-ycb-object-and-model-set-towards-common-benchmarks-for-1vhlq6qrm5.pdf

The YCB object and Model set: Towards common benchmarks for manipulation research

Tactile sensing in dexterous robot hands - Review

In this paper, we present a new humanoid robot currently being developed for applications in human-centered environments In order for humanoid robots to enter human-centered environments, it is indispensable to equip them with manipulative, perceptive and communicative skills necessary for real-time interaction with the environment and humans The goal of our work is to provide reliable and highly integrated humanoid platforms which on the one hand allow the implementation and tests of various research activities and on the other hand the realization of service tasks in a household scenario We introduce the different subsystems of the robot We present the kinematics, sensors, and the hardware and software architecture We propose a hierarchically organized architecture and introduce the mapping of the functional features in this architecture into hardware and software modules We also describe different skills related to real-time object localization and motor control, which have been realized and integrated into the entire control architecture

/pdf/armar-iii-an-integrated-humanoid-platform-for-sensory-motor-2txpwtctal.pdf

ARMAR-III: An Integrated Humanoid Platform for Sensory-Motor Control

https://his.anthropomatik.kit.edu/pdf_humanoids/Asfour2008b.pdf

Toward humanoid manipulation in human-centred environments

This paper presents the new robotic FRH-4 hand. The FRH-4 hand constitutes a new hybrid concept of an anthropomorphic five fingered hand and a three jaw robotic gripper. The hand has a humanoid appearance while maintaining the precision of a robotic gripper. Since it is actuated with flexible fluidic actuators, it exhibits an excellent power to weight ratio. These elastic actuators also ensure that the hand is safe for interacting with humans. In order to fully control the joints, it is equipped with position sensors on all of the 11 joints. The hand is also fitted with tactile sensors based on cursor navigation sensor elements, which allows it to have grasping feedback and the ability for exploration.

A new anthropomorphic robotic hand

Haptic exploration of unknown objects is of great importance for acquiring multi-modal object representations, which enable a humanoid robot to autonomously execute grasping and manipulation tasks. In this paper we present a tactile exploration strategy to guide an anthropomorphic five-finger hand along the surface of previously unknown objects and build a 3D object representation based on acquired tactile point clouds. The proposed strategy makes use of the dynamic potential field approach suggested in the context of mobile robot navigation. To demonstrate the capabilities of this strategy, we conduct experiments in a detailed physics simulation using a model of the five-finger hand. Exploration results of several test objects are given.

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A potential field approach to dexterous tactile exploration of unknown objects

In this work, we present a new software environment for the comparative evaluation of algorithms for grasping and dexterous manipulation. The key aspect in its development is to provide a tool that allows the reproduction of well-defined experiments in real-life scenarios in every laboratory and, hence, benchmarks that pave the way for objective comparison and competition in the field of grasping. In order to achieve this, experiments are performed on a sound open-source software platform with an extendable structure in order to be able to include a wider range of benchmarks defined by robotics researchers. The environment is integrated into the OpenGRASP toolkit that is built upon the OpenRAVE project and includes grasp-specific extensions and a tool for the creation/integration of new robot models. Currently, benchmarks for grasp and motion planningare included as case studies, as well as a library of domestic everyday objects models, and a real-life scenario that features a humanoid robot acting in a kitchen.

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Alexander Bierbaum

Papers

ARMAR-III: An Integrated Humanoid Platform for Sensory-Motor Control

Toward humanoid manipulation in human-centred environments

A new anthropomorphic robotic hand

A potential field approach to dexterous tactile exploration of unknown objects

The OpenGRASP benchmarking suite: An environment for the comparative analysis of grasping and dexterous manipulation