1 This article presents a novel methodology for a robot to autonomously induce models of its actions and sensors called asami (Autonomous Sensor and Actuator Model Induction). While previous approaches to model learning rely on an independent source of training data, we show how a robot can induce action and sensor models without any well-calibrated feedback. Specif-ically, the only inputs to the asami learning process are the data the robot would naturally have access to: its raw sensations and knowledge of its own action selections. From the per-spective of developmental robotics, our robot’s goal is to obtain self-consistent internal models, rather than to perform any externally defined tasks. Furthermore, the target function of each model-learning process comes from within the system, namely the most current version of an-other internal system model. Concretely realizing this model-learning methodology presents a number of challenges, and we introduce a broad class of settings in which solutions to these challenges are presented. asami is fully implemented and tested, and empirical results validate our approach in a robotic testbed domain using a Sony Aibo ERS-7 robot.

We present a novel interface for human-robot interaction that enables a human to intuitively and unambiguously select a 3D location in the world and communicate it to a mobile robot. The human points at a location of interest and illuminates it (“clicks it”) with an unaltered, off-the-shelf, green laser pointer. The robot detects the resulting laser spot with an omnidirectional, catadioptric camera with a narrow-band green filter. After detection, the robot moves its stereo pan/tilt camera to look at this location and estimates the location’s 3D position with respect to the robot’s frame of reference. Unlike previous approaches, this interface for gesture-based pointing requires no instrumentation of the environment, makes use of a non-instrumented everyday pointing device, has low spatial error out to 3 meters, is fully mobile, and is robust enough for use in real-world applications. We demonstrate that this human-robot interface enables a person to designate a wide variety of everyday objects placed throughout a room. In 99.4 % of these tests, the robot successfully looked at the designated object and estimated its 3D position with low average error. We also show that this interface can support object acquisition by a mobile manipulator. For this application, the user selects an object to be picked up from the floor by “clicking ” on it with the laser pointer interface. In 90 % of these trials, the robot successfully moved to the designated object and picked it up off of the floor.

Abstract While complex hands seem to offer generality, simple hands are more practical for most robotic and telerobotic manipulation tasks, and will remain so for the foreseeable future. This raises the question: how do generality and simplicity trade off in the design of robot hands? This paper explores the tension between simplicity in hand design and generality in hand function. It raises arguments both for and against simple hands; it considers several familiar examples; and it proposes a concept for a simple hand design with associated strategies for grasping and object localization. The central idea is to use knowledge of stable grasp poses as a cue for object localization. This leads to some novel design criteria, such as a desire to have only a few stable grasp poses. We explore some of the design implications for a binpicking task, and then examine some experimental results to see how this approach might be applied in an assistive object retrieval task. 1

We have experimented with proprioception in a bio-inspired self-organizing haptic system. To this end a 12 d.o.f. anthropomorphic robot hand with proprioceptive sensors was developed. The system uses a self-organizing map for the mapping of the explored objects. In our experiments the system was trained and tested with 10 different objects of different sizes from two different shape categories. To estimate the generalization ability the system was also tested with 6 new objects. The system showed good performance with the objects from both the training set as well as in the generalization experiment. In both cases the system was able to discriminate the shape, the size and to some extent the individual objects. 1.

Abstract — We present a neurally implemented control system where a robot grasps an object while being guided by the visually perceived position of the object. The system consists of three parts operating in a series: (i) A simplified visual system with a what-where pathway localizes the target object in the visual field. (ii) A coordinate transformation network considers the visually perceived object position and the camera pan-tilt angle to compute the target position in a body-centered frame of reference, as needed for motor action. (iii) This body-centered position is then used by a reinforcement-trained network which docks the robot at a table so that it can grasp the object. The novel coordinate transformation network which we describe in detail here allows for a complicated body geometry in which an agent’s sensors such as a camera can be moved with respect to the body, just like the human head and eyes can. The network is trained, allowing a wide range of transformations that need not be implemented by geometrical calculations. Index Terms — Neural Networks, Frame of Reference Transformations I.

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Robots and humans receive partial, fragmentary hints about the world’s state through their respective sensors. In this paper, we focus on some fundamental problems in perception that have attracted the attention of researchers in both robotics and infant development: object segregation, intermodal inte-gration, and the role of embodiment. We concentrate on identifying points of contact between the two fields, and also important questions identified in one field and not yet addressed in the other. For object segregation, both fields have examined the idea of using “key events ” where perception is in some way simplified and the infant or robot acquires knowledge that can be exploited at other times. We examine this parallel research in some detail. We propose that the identification of the key events themselves constitutes a point of contact between the fields. And although the specific algorithms used in robots are not easy to relate to infant development, the overall “algorithmic skeleton ” formed by the set of algorithms needed to identify and exploit key events may in fact form a basis for mutual dialogue.

Robots and humans receive partial, fragmentary hints about the world’s state through their respective sensors. These hints – tiny patches of light intensity, frequency components of sound, etc. – are far removed from the world of objects we feel we perceive so effortlessly around us. The study of infant development and the construction of robots are both deeply concerned with how this apparent gap between the world and our experience of it is bridged. In this paper, we focus on some fundamental problems in perception that have attracted the attention of researchers in both robotics and infant development. Our goal is to identify points of contact already existing between the two fields, and also important questions identified in one field that could fruitfully be addressed in the other. We start with the problem of object segregation: how do infants and robots determine visually where one object ends and another begins? For object segregation, both fields have examined the idea of using “key events ” where perception is in some way simplified and the infant or robot acquires knowledge that can be exploited at other times. We propose that the identification of the key events themselves constitutes a point of contact between the fields. And although the specific algorithms used in robots do not necessarily map directly to infant strategies, the overall “algorithmic skeleton ” formed by the set of algorithms needed to identify and exploit key events may in fact form a basis for mutual dialogue. We then look more broadly at the role of embodiment in humans and robots, and see the opportunities it affords for development.

The Lucs Haptic Hand III has been built as a step in a project at LUCS aiming at studying haptic perception. In this project, several robot hands together with cognitive computational models of the corresponding human neurophysiologic systems will be built. Grasping tests with the LUCS Haptic Hand III were done with six different objects in order to get a comprehension of the signal patterns from the proprioceptive sensors provided while grasping. The results from these preliminary grasping tests suggest that the LUCS Haptic Hand III provides signal patterns rich enough to serve in our current haptic models. 1

Abstract — Object manipulation is one of the important tasks in robotics. Tactile sensor system is essential as a sensory device to support robot control system. This paper present a precision control scheme of multi-fingered humanoid robot arm based on tactile sensing information to perform object manipulation tasks. With the aim to enhance the ability to recognize and manipulate object in humanoid robot, we developed a novel optical three-axis tactile sensor system mounted on fingertips of the humanoid robot fingers. This tactile sensor applies an optical waveguide transduction method, and capable of acquiring normal and shearing force. Trajectory generation based on kinematical solutions at the arm and fingers, together with control system structure and sensing principle of the tactile sensor system are presented. Object manipulation experiments are conducted using hard and soft objects. Experimental results revealed that the proposed control scheme enable the finger system to recognize low force interactions based on tactile sensing information to grasp the object surface and manipulate it without causing damage to the object and the sensor elements. Index Terms—Tactile sensing, optical waveguide, object manipulation, multi-fingered arm, humanoid robot. I.