18,523 research outputs found
On inferring intentions in shared tasks for industrial collaborative robots
Inferring human operators' actions in shared collaborative tasks, plays a crucial role in enhancing the cognitive capabilities of industrial robots. In all these incipient collaborative robotic applications, humans and robots not only should share space but also forces and the execution of a task. In this article, we present a robotic system which is able to identify different human's intentions and to adapt its behavior consequently, only by means of force data. In order to accomplish this aim, three major contributions are presented: (a) force-based operator's intent recognition, (b) force-based dataset of physical human-robot interaction and (c) validation of the whole system in a scenario inspired by a realistic industrial application. This work is an important step towards a more natural and user-friendly manner of physical human-robot interaction in scenarios where humans and robots collaborate in the accomplishment of a task.Peer ReviewedPostprint (published version
Introduction: The Fourth International Workshop on Epigenetic Robotics
As in the previous editions, this workshop is trying to be a forum for multi-disciplinary research ranging from developmental psychology to neural sciences (in its widest sense) and robotics including computational studies. This is a two-fold aim of, on the one hand, understanding the brain through engineering embodied systems and, on the other hand, building artificial epigenetic systems. Epigenetic contains in its meaning the idea that we are interested in studying development through interaction with the environment. This idea entails the embodiment of the system, the situatedness in the environment, and of course a prolonged period of postnatal development when this interaction can actually take place. This is still a relatively new endeavor although the seeds of the developmental robotics community were already in the air since the nineties (Berthouze and Kuniyoshi, 1998; Metta et al., 1999; Brooks et al., 1999; Breazeal, 2000; Kozima and Zlatev, 2000). A few had the intuition â see Lungarella et al. (2003) for a comprehensive review â that, intelligence could not be possibly engineered simply by copying systems that are âready madeâ but rather that the development of the system fills a major role. This integration of disciplines raises the important issue of learning on the multiple scales of developmental time, that is, how to build systems that eventually can learn in any environment rather than program them for a specific environment. On the other hand, the hope is that robotics might become a new tool for brain science similarly to what simulation and modeling have become for the study of the motor system. Our community is still pretty much evolving and âunder constructionâ and for this reason, we tried to encourage submissions from the psychology community. Additionally, we invited four neuroscientists and no roboticists for the keynote lectures. We received a record number of submissions (more than 50), and given the overall size and duration of the workshop together with our desire to maintain a single-track format, we had to be more selective than ever in the review process (a 20% acceptance rate on full papers). This is, if not an index of quality, at least an index of the interest that gravitates around this still new discipline
From Imprinting to Adaptation: Building a History of Affective Interaction
We present a Perception-Action architecture and experiments to simulate imprintingâthe establishment of strong attachment links with a âcaregiverââin a robot. Following recent theories, we do not consider imprinting as rigidly timed and irreversible, but as a more flexible phenomenon that allows for further adaptation as a result of reward-based learning through experience. Our architecture reconciles these two types of perceptual learning traditionally considered as different and even incompatible. After the initial imprinting, adaptation is achieved in the context of a history of âaffectiveâ interactions between the robot and a human, driven by âdistressâ and âcomfortâ responses in the robot
A Whole-Body Pose Taxonomy for Loco-Manipulation Tasks
Exploiting interaction with the environment is a promising and powerful way
to enhance stability of humanoid robots and robustness while executing
locomotion and manipulation tasks. Recently some works have started to show
advances in this direction considering humanoid locomotion with multi-contacts,
but to be able to fully develop such abilities in a more autonomous way, we
need to first understand and classify the variety of possible poses a humanoid
robot can achieve to balance. To this end, we propose the adaptation of a
successful idea widely used in the field of robot grasping to the field of
humanoid balance with multi-contacts: a whole-body pose taxonomy classifying
the set of whole-body robot configurations that use the environment to enhance
stability. We have revised criteria of classification used to develop grasping
taxonomies, focusing on structuring and simplifying the large number of
possible poses the human body can adopt. We propose a taxonomy with 46 poses,
containing three main categories, considering number and type of supports as
well as possible transitions between poses. The taxonomy induces a
classification of motion primitives based on the pose used for support, and a
set of rules to store and generate new motions. We present preliminary results
that apply known segmentation techniques to motion data from the KIT whole-body
motion database. Using motion capture data with multi-contacts, we can identify
support poses providing a segmentation that can distinguish between locomotion
and manipulation parts of an action.Comment: 8 pages, 7 figures, 1 table with full page figure that appears in
landscape page, 2015 IEEE/RSJ International Conference on Intelligent Robots
and System
Human Like Adaptation of Force and Impedance in Stable and Unstable Tasks
AbstractâThis paper presents a novel human-like learning con-troller to interact with unknown environments. Strictly derived from the minimization of instability, motion error, and effort, the controller compensates for the disturbance in the environment in interaction tasks by adapting feedforward force and impedance. In contrast with conventional learning controllers, the new controller can deal with unstable situations that are typical of tool use and gradually acquire a desired stability margin. Simulations show that this controller is a good model of human motor adaptation. Robotic implementations further demonstrate its capabilities to optimally adapt interaction with dynamic environments and humans in joint torque controlled robots and variable impedance actuators, with-out requiring interaction force sensing. Index TermsâFeedforward force, human motor control, impedance, robotic control. I
Evolution of Prehension Ability in an Anthropomorphic Neurorobotic Arm
In this paper we show how a simulated anthropomorphic robotic arm controlled by an artificial neural network can develop effective reaching and grasping behaviour through a trial and error process in which the free parameters encode the control rules which regulate the fine-grained interaction between the robot and the environment and variations of the free parameters are retained or discarded on the basis of their effects at the level of the global behaviour exhibited by the robot situated in the environment. The obtained results demonstrate how the proposed methodology allows the robot to produce effective behaviours thanks to its ability to exploit the morphological properties of the robotâs body (i.e. its anthropomorphic shape, the elastic properties of its muscle-like actuators, and the compliance of its actuated joints) and the properties which arise from the physical interaction between the robot and the environment mediated by appropriate control rules
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