[Show abstract][Hide abstract] ABSTRACT: In this work we propose a comprehensive framework for the
gaze stabilization of humanoid robots that is capable of
compensating for the motion induced in the camera images
due to the auto-generated movements of the robot so as to
the external disturbances acting on its body. We first
provide an extensive mathematical formulation to derive the
forward and the differential kinematics of the fixation
point, given the mechanism actuating the coupled eyes, and
then we employ two separate signals for stabilization
purpose: (1) the anticipatory term obtained from the
velocity commands sent to the joints while the robot is
moving autonomously; (2) the feedback term represented by
the data acquired from the on board gyroscope that serve to
react against unpredicted disturbances. We finally test our
method on the iCub robot showing how the residual optical
flow measured from the sequence of camera images is kept
significantly low while the robot moves following the
planned trajectory and/or varies its posture upon external
2014 IEEE-RAS International Conference on Humanoid Robots; 11/2014
[Show abstract][Hide abstract] ABSTRACT: Systematically developing high--quality reusable software components is a
difficult task and requires careful design to find a proper balance between
potential reuse, functionalities and ease of implementation. Extendibility is
an important property for software which helps to reduce cost of development
and significantly boosts its reusability. This work introduces an approach to
enhance components reusability by extending their functionalities using
plug-ins at the level of the connection points (ports). Application--dependent
functionalities such as data monitoring and arbitration can be implemented
using a conventional scripting language and plugged into the ports of
components. The main advantage of our approach is that it avoids to introduce
application--dependent modifications to existing components, thus reducing
development time and fostering the development of simpler and therefore more
reusable components. Another advantage of our approach is that it reduces
communication and deployment overheads as extra functionalities can be added
without introducing additional modules.
[Show abstract][Hide abstract] ABSTRACT: This paper deals with the problem of 3D stereo estimation and eye-hand calibration in humanoid robots. We first show how to implement a complete 3D stereo vision pipeline, enabling online and real-time eye calibration. We then introduce a new formulation for the problem of eye-hand coordination. We developed a fully automated procedure that does not require human supervision. The end-effector of the humanoid robot is automatically detected in the stereo images, providing large amounts of training data for learning the vision-to-kinematics mapping. We report exhaustive experiments using different machine learning techniques; we show that a mixture of linear transformations can achieve the highest accuracy in the shortest amount of time, while guaranteeing real-time performance. We demonstrate the application of the proposed system in two typical robotic scenarios: (1) object grasping and tool use; (2) 3D scene reconstruction. The platform of choice is the iCub humanoid robot.
IEE-RAS International Conference on Humanoid Robots; 11/2014
[Show abstract][Hide abstract] ABSTRACT: This paper presents a new technique to control highly redundant mechanical
systems, such as humanoid robots. We take inspiration from two approaches.
Prioritized control is a widespread multi-task technique in robotics and
animation: tasks have strict priorities and they are satisfied only as long as
they do not conflict with any higher-priority task. Optimal control instead
formulates an optimization problem whose solution is either a feedback control
policy or a feedforward trajectory of control inputs. We introduce strict
priorities in multi-task optimal control problems, as an alternative to
weighting task errors proportionally to their importance. This ensures the
respect of the specified priorities, while avoiding numerical conditioning
issues. We compared our approach with both prioritized control and optimal
control with tests on a simulated robot with 11 degrees of freedom.
[Show abstract][Hide abstract] ABSTRACT: Legged robots are typically in rigid contact with the environment at multiple
locations, which add a degree of complexity to their control. We present a
method to control the motion and a subset of the contact forces of a
floating-base robot. We derive a new formulation of the lexicographic
optimization problem typically arising in multitask motion/force control
frameworks. The structure of the constraints of the problem (i.e. the dynamics
of the robot) allows us to find a sparse analytical solution. This leads to an
equivalent optimization with reduced computational complexity, comparable to
inverse-dynamics based approaches. At the same time, our method preserves the
flexibility of optimization based control frameworks. Simulations were carried
out to achieve different multi-contact behaviors on a 23-degree-offreedom
humanoid robot, validating the presented approach. A comparison with another
state-of-the-art control technique with similar computational complexity shows
the benefits of our controller, which can eliminate force/torque
[Show abstract][Hide abstract] ABSTRACT: We present a new framework for prioritized multi-task motion/force control of fully-actuated robots. This work is established on a careful review and comparison of the state of the art. Some control frameworks are not optimal, that is they do not find the optimal solution for the secondary tasks. Other frameworks are optimal, but they tackle the control problem at kinematic level, hence they neglect the robot dynamics and they do not allow for force control. Still other frameworks are optimal and consider force control, but they are computationally less efficient than ours. Our final claim is that, for fully-actuated robots, computing the operational-space inverse dynamics is equivalent to computing the inverse kinematics (at acceleration level) and then the joint-space inverse dynamics. Thanks to this fact, our control framework can efficiently compute the optimal solution by decoupling kinematics and dynamics of the robot. We take into account: motion and force control, soft and rigid contacts, free and constrained robots. Tests in simulation validate our control framework, comparing it with other state-of-the-art equivalent frameworks and showing remarkable improvements in optimality and efficiency.
Robotics and Autonomous Systems 10/2014; · 1.16 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: This paper proposes a learning from demonstration system based on a motion feature, called phase transfer sequence. The system aims to synthesize the knowledge on humanoid whole body motions learned during teacher-supported interactions, and apply this knowledge during different physical interactions between a robot and its surroundings. The phase transfer sequence represents the temporal order of the changing points in multiple time sequences. It encodes the dynamical aspects of the sequences so as to absorb the gaps in timing and amplitude derived from interaction changes. The phase transfer sequence was evaluated in reinforcement learning of sitting-up and walking motions conducted by a real humanoid robot and compatible simulator. In both tasks, the robotic motions were less dependent on physical interactions when learned by the proposed feature than by conventional similarity measurements. Phase transfer sequence also enhanced the convergence speed of motion learning. Our proposed feature is original primarily because it absorbs the gaps caused by changes of the originally acquired physical interactions, thereby enhancing the learning speed in subsequent interactions.
IEEE transactions on neural networks and learning systems 07/2014; · 4.37 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Flexible sensors are gaining increasing interest in a number of applications, including biomedical, food control, domotics and robotics, having very light weight, robustness and low cost. Low temperature polycrystalline silicon (LTPS) technology is particularly attractive for such applications, since LTPS TFTs show excellent electrical characteristics, good stability and offer the possibility to exploit CMOS architectures. Then, as examples of flexible sensing systems, we present a tactile sensor for robotic applications and a pH sensor for biomedical applications. The present results can pave the way to advanced flexible sensing systems, where sensors and local signal conditioning circuits can be integrated on the same flexible substrate.
2014 21st International Workshop on Active-Matrix Flatpanel Displays and Devices (AM-FPD); 07/2014
[Show abstract][Hide abstract] ABSTRACT: Human expertise in face perception grows over development, but even within minutes of birth, infants exhibit an extraordinary sensitivity to face-like stimuli. The dominant theory accounts for innate face detection by proposing that the neonate brain contains an innate face detection device, dubbed 'Conspec'. Newborn face preference has been promoted as some of the strongest evidence for innate knowledge, and forms a canonical stage for the modern form of the nature-nurture debate in psychology. Interpretation of newborn face preference results has concentrated on monocular stimulus properties, with little mention or focused investigation of potential binocular involvement. However, the question of whether and how newborns integrate the binocular visual streams bears directly on the generation of observable visual preferences. In this theoretical paper, we employ a synthetic approach utilizing robotic and computational models to draw together the threads of binocular integration and face preference in newborns, and demonstrate cases where the former may explain the latter. We suggest that a system-level view considering the binocular embodiment of newborn vision may offer a mutually satisfying resolution to some long-running arguments in the polarizing debate surrounding the existence and causal structure of newborns' 'innate knowledge' of faces.
[Show abstract][Hide abstract] ABSTRACT: Action perception and recognition are core abilities fundamental for human social interaction. A parieto-frontal network (the mirror neuron system) matches visually presented biological motion information onto observers' motor representations. This process of matching the actions of others onto our own sensorimotor repertoire is thought to be important for action recognition, providing a non-mediated "motor perception" based on a bidirectional flow of information along the mirror parieto-frontal circuits. State-of-the-art machine learning strategies for hand action identification have shown better performances when sensorimotor data, as opposed to visual information only, are available during learning. As speech is a particular type of action (with acoustic targets), it is expected to activate a mirror neuron mechanism. Indeed, in speech perception, motor centers have been shown to be causally involved in the discrimination of speech sounds. In this paper, we review recent neurophysiological and machine learning-based studies showing (a) the specific contribution of the motor system to speech perception and (b) that automatic phone recognition is significantly improved when motor data are used during training of classifiers (as opposed to learning from purely auditory data).
Topics in Cognitive Science 06/2014; 6(3):461-475. · 2.88 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: This article presents results from a multidisciplinary research project on the integration and transfer of language knowledge into robots as an empirical paradigm for the study of language development in both humans and humanoid robots. Within the framework of human linguistic and cognitive development, we focus on how three central types of learning interact and co-develop: individual learning about one's own embodiment and the environment, social learning (learning from others), and learning of linguistic capability. Our primary concern is how these capabilities can scaffold each other's development in a continuous feedback cycle as their interactions yield increasingly sophisticated competencies in the agent's capacity to interact with others and manipulate its world. Experimental results are summarized in relation to milestones in human linguistic and cognitive development and show that the mutual scaffolding of social learning, individual learning, and linguistic capabilities creates the context, conditions, and requisites for learning in each domain. Challenges and insights identified as a result of this research program are discussed with regard to possible and actual contributions to cognitive science and language ontogeny. In conclusion, directions for future work are suggested that continue to develop this approach toward an integrated framework for understanding these mutually scaffolding processes as a basis for language development in humans and robots.
Topics in Cognitive Science 06/2014; · 2.88 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Calibration continues to receive significant atten-tion in robotics because of its key impact on performance and cost associated with the operation of complex robots. Calibration of kinematic parameters is typically the first mandatory step. To this end, a variety of metrology systems and corresponding algorithms have been described in the literature relying on measurements of the pose of the end-effector using a camera or laser tracking system, or, exploiting constraints arising from contacts of the end-effector with the environment. In this work, we take inspiration from the behavior of infants and certain animals, who are believed to use self-stimulation or self-touch to "calibrate" their body representations, and present a new solution to this problem by letting the robot close the kinematic chain by touching its own body. The robot considered in this paper is sensorized with tactile arrays for a total of about 4200 sensing points. The correspondence between the predicted contact point from existing forward kinematics and the actual position on the robot's 'skin' provides sample data that allows refining the kinematic representation (DH param-eters). The data collection procedure is automated—self-touch is autonomously executed by the robot—and can be repeated at any time, providing a compact self-calibration system that does not require an external measurement apparatus.
Proc. IEEE Int. Conf. Robotics and Automation (ICRA), Hong Kong, China; 06/2014
[Show abstract][Hide abstract] ABSTRACT: In this paper we propose a weighted supervised pooling method for visual recognition systems. We combine a standard Spatial Pyramid Representation which is commonly adopted to encode spatial information, with an appropriate Feature Space Representation favoring semantic information in an appropriate feature space. For the latter, we propose a weighted pooling strategy exploiting data supervision to weigh each local descriptor coherently with its likelihood to belong to a given object class. The two representations
are then combined adaptively with Multiple Kernel Learning. Experiments on common benchmarks (Caltech-
256 and PASCAL VOC-2007) show that our image representation improves the current visual recognition pipeline and it is competitive with similar state-of-art pooling methods. We also evaluate our method on a real Human-Robot Interaction setting, where the pure Spatial Pyramid Representation does not provide sufficient discriminative power, obtaining a remarkable improvement.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 06/2014
[Show abstract][Hide abstract] ABSTRACT: This paper presents a new technique to control highly redundant mechanical systems, such as humanoid robots. We take inspiration from two approaches. Prioritized control is a widespread multi-task technique in robotics and animation: tasks have strict priorities and they are satisfied only as long as they do not conflict with any higher-priority task. Optimal control instead formulates an optimization problem whose solution is either a feedback control policy or a feedforward trajectory of control inputs. We introduce strict priorities in multi-task optimal control problems, as an alternative to weighting task errors proportionally to their importance. This ensures the respect of the specified priorities, while avoiding numerical conditioning issues. We compared our approach with both prioritized control and optimal control with tests on a simulated robot with 11 degrees of freedom.
Robotics and Automation, IEEE International Conference on (ICRA), Hong Kong, China; 05/2014
[Show abstract][Hide abstract] ABSTRACT: In this paper we propose an autoencoder-based method for the unsupervised identification of subword units. We experiment with different types and architectures of autoencoders to asses what autoencoder properties are most important for this task. We first show that the encoded representation of speech pro-duced by standard autencoders is more effective than Gaus-sian posteriorgrams in a spoken query classification task. Fi-nally we evaluate the subword inventories produced by the proposed method both in terms of classification accuracy in a word classification task (with lexicon size up to 263 words) and in terms of consistency between subword transcription of different word examples of a same word type. The evaluation is carried out on Italian and American English datasets.
IEEE Internation Conference on Acoustics, Speech and Language Processing; 05/2014