2,160 research outputs found
Visual Perception for Manipulation and Imitation in Humanoid Robots
This thesis deals with visual perception for manipulation and imitation in humanoid robots. In particular, real-time applicable methods for object recognition and pose estimation as well as for markerless human motion capture have been developed. As only sensor a small baseline stereo camera system (approx. human eye distance) was used. An extensive experimental evaluation has been performed on simulated as well as real image data from real-world scenarios using the humanoid robot ARMAR-III
Beyond Gazing, Pointing, and Reaching: A Survey of Developmental Robotics
Developmental robotics is an emerging field located
at the intersection of developmental psychology
and robotics, that has lately attracted
quite some attention. This paper gives a survey of
a variety of research projects dealing with or inspired
by developmental issues, and outlines possible
future directions
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
Learning at the Ends: From Hand to Tool Affordances in Humanoid Robots
One of the open challenges in designing robots that operate successfully in
the unpredictable human environment is how to make them able to predict what
actions they can perform on objects, and what their effects will be, i.e., the
ability to perceive object affordances. Since modeling all the possible world
interactions is unfeasible, learning from experience is required, posing the
challenge of collecting a large amount of experiences (i.e., training data).
Typically, a manipulative robot operates on external objects by using its own
hands (or similar end-effectors), but in some cases the use of tools may be
desirable, nevertheless, it is reasonable to assume that while a robot can
collect many sensorimotor experiences using its own hands, this cannot happen
for all possible human-made tools.
Therefore, in this paper we investigate the developmental transition from
hand to tool affordances: what sensorimotor skills that a robot has acquired
with its bare hands can be employed for tool use? By employing a visual and
motor imagination mechanism to represent different hand postures compactly, we
propose a probabilistic model to learn hand affordances, and we show how this
model can generalize to estimate the affordances of previously unseen tools,
ultimately supporting planning, decision-making and tool selection tasks in
humanoid robots. We present experimental results with the iCub humanoid robot,
and we publicly release the collected sensorimotor data in the form of a hand
posture affordances dataset.Comment: dataset available at htts://vislab.isr.tecnico.ulisboa.pt/, IEEE
International Conference on Development and Learning and on Epigenetic
Robotics (ICDL-EpiRob 2017
Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics
âThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." âCopyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.âThis position paper proposes that the study of embodied cognitive agents, such as humanoid robots, can advance our understanding of the cognitive development of complex sensorimotor, linguistic, and social learning skills. This in turn will benefit the design of cognitive robots capable of learning to handle and manipulate objects and tools autonomously, to cooperate and communicate with other robots and humans, and to adapt their abilities to changing internal, environmental, and social conditions. Four key areas of research challenges are discussed, specifically for the issues related to the understanding of: 1) how agents learn and represent compositional actions; 2) how agents learn and represent compositional lexica; 3) the dynamics of social interaction and learning; and 4) how compositional action and language representations are integrated to bootstrap the cognitive system. The review of specific issues and progress in these areas is then translated into a practical roadmap based on a series of milestones. These milestones provide a possible set of cognitive robotics goals and test scenarios, thus acting as a research roadmap for future work on cognitive developmental robotics.Peer reviewe
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
Interactive Robot Learning of Gestures, Language and Affordances
A growing field in robotics and Artificial Intelligence (AI) research is
human-robot collaboration, whose target is to enable effective teamwork between
humans and robots. However, in many situations human teams are still superior
to human-robot teams, primarily because human teams can easily agree on a
common goal with language, and the individual members observe each other
effectively, leveraging their shared motor repertoire and sensorimotor
resources. This paper shows that for cognitive robots it is possible, and
indeed fruitful, to combine knowledge acquired from interacting with elements
of the environment (affordance exploration) with the probabilistic observation
of another agent's actions.
We propose a model that unites (i) learning robot affordances and word
descriptions with (ii) statistical recognition of human gestures with vision
sensors. We discuss theoretical motivations, possible implementations, and we
show initial results which highlight that, after having acquired knowledge of
its surrounding environment, a humanoid robot can generalize this knowledge to
the case when it observes another agent (human partner) performing the same
motor actions previously executed during training.Comment: code available at https://github.com/gsaponaro/glu-gesture
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
- âŠ