7,659 research outputs found
Predicting the internal model of a robotic system from its morphology
The estimation of the internal model of a robotic system results from the interaction of its morphology, sensors and
actuators, with a particular environment. Model learning techniques, based on supervised machine learning, are
widespread for determining the internal model. An important limitation of such approaches is that once a model has
been learnt, it does not behave properly when the robot morphology is changed. From this it follows that there must
exist a relationship between them. We propose a model for this correlation between the morphology and the internal
model parameters, so that a new internal model can be predicted when the morphological parameters are modified.
Di erent neural network architectures are proposed to address this high dimensional regression problem. A case
study is analyzed in detail to illustrate and evaluate the performance of the approach, namely, a pan-tilt robot head
executing saccadic movements. The best results are obtained for an architecture with parallel neural networks due
to the independence of its outputs. Theses results can have a great significance since the predicted parameters can
dramatically speed up the adaptation process following a change in morpholog
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
Flora robotica -- An Architectural System Combining Living Natural Plants and Distributed Robots
Key to our project flora robotica is the idea of creating a bio-hybrid system
of tightly coupled natural plants and distributed robots to grow architectural
artifacts and spaces. Our motivation with this ground research project is to
lay a principled foundation towards the design and implementation of living
architectural systems that provide functionalities beyond those of orthodox
building practice, such as self-repair, material accumulation and
self-organization. Plants and robots work together to create a living organism
that is inhabited by human beings. User-defined design objectives help to steer
the directional growth of the plants, but also the system's interactions with
its inhabitants determine locations where growth is prohibited or desired
(e.g., partitions, windows, occupiable space). We report our plant species
selection process and aspects of living architecture. A leitmotif of our
project is the rich concept of braiding: braids are produced by robots from
continuous material and serve as both scaffolds and initial architectural
artifacts before plants take over and grow the desired architecture. We use
light and hormones as attraction stimuli and far-red light as repelling
stimulus to influence the plants. Applied sensors range from simple proximity
sensing to detect the presence of plants to sophisticated sensing technology,
such as electrophysiology and measurements of sap flow. We conclude by
discussing our anticipated final demonstrator that integrates key features of
flora robotica, such as the continuous growth process of architectural
artifacts and self-repair of living architecture.Comment: 16 pages, 12 figure
Robots as Powerful Allies for the Study of Embodied Cognition from the Bottom Up
A large body of compelling evidence has been accumulated demonstrating that embodiment – the agent’s physical setup, including its shape, materials, sensors and actuators – is constitutive for any form of cognition and as a consequence, models of cognition need to be embodied. In contrast to methods from empirical sciences to study cognition, robots can be freely manipulated and virtually all key variables of their embodiment and control programs can be systematically varied. As such, they provide an extremely powerful tool of investigation. We present a robotic bottom-up or developmental approach, focusing on three stages: (a) low-level behaviors like walking and reflexes, (b) learning regularities in sensorimotor spaces, and (c) human-like cognition. We also show that robotic based research is not only a productive path to deepening our understanding of cognition, but that robots can strongly benefit from human-like cognition in order to become more autonomous, robust, resilient, and safe
Bending angle prediction and control of soft pneumatic actuators with embedded flex sensors - a data-driven approach
In this paper, a purely data-driven modelling approach is presented for predicting and controlling the free bending angle response of a typical soft pneumatic actuator (SPA), embedded with a resistive flex sensor. An experimental setup was constructed to test the SPA at different input pressure values and orientations, while recording the resulting feedback from the embedded flex sensor and on-board pressure sensor. A calibrated high speed camera captures image frames during the actuation, which are then analysed using an image processing program to calculate the actual bending angle and synchronise it with the recorded sensory feedback. Empirical models were derived based on the generated experimental data using two common data-driven modelling techniques; regression analysis and artificial neural networks. Both techniques were validated using a new dataset at untrained operating conditions to evaluate their prediction accuracy. Furthermore, the derived empirical model was used as part of a closed-loop PID controller to estimate and control the bending angle of the tested SPA based on the real-time sensory feedback generated. The tuned PID controller allowed the bending SPA to accurately follow stepped and sinusoidal reference signals, even in the presence of pressure leaks in the pneumatic supply. This work demonstrates how purely data-driven models can be effectively used in controlling the bending of SPAs under different operating conditions, avoiding the need for complex analytical modelling and material characterisation. Ultimately, the aim is to create more controllable soft grippers based on such SPAs with embedded sensing capabilities, to be used in applications requiring both a ‘soft touch’ as well as a more controllable object manipulation
Fast Damage Recovery in Robotics with the T-Resilience Algorithm
Damage recovery is critical for autonomous robots that need to operate for a
long time without assistance. Most current methods are complex and costly
because they require anticipating each potential damage in order to have a
contingency plan ready. As an alternative, we introduce the T-resilience
algorithm, a new algorithm that allows robots to quickly and autonomously
discover compensatory behaviors in unanticipated situations. This algorithm
equips the robot with a self-model and discovers new behaviors by learning to
avoid those that perform differently in the self-model and in reality. Our
algorithm thus does not identify the damaged parts but it implicitly searches
for efficient behaviors that do not use them. We evaluate the T-Resilience
algorithm on a hexapod robot that needs to adapt to leg removal, broken legs
and motor failures; we compare it to stochastic local search, policy gradient
and the self-modeling algorithm proposed by Bongard et al. The behavior of the
robot is assessed on-board thanks to a RGB-D sensor and a SLAM algorithm. Using
only 25 tests on the robot and an overall running time of 20 minutes,
T-Resilience consistently leads to substantially better results than the other
approaches
Data-driven bending angle prediction of soft pneumatic actuators with embedded flex sensors
In this paper, resistive flex sensors have been embedded at the strain limiting layer of soft
pneumatic actuators, in order to provide sensory feedback that can be utilised in predicting their bending
angle during actuation. An experimental setup was prepared to test the soft actuators under controllable
operating conditions, record the resulting sensory feedback, and synchronise this with the actual bending
angles measured using a developed image processing program. Regression analysis and neural networks
are two data-driven modelling techniques that were implemented and compared in this study, to evaluate
their ability in predicting the bending angle response of the tested soft actuators at different input
pressures and testing orientations. This serves as a step towards controlling this class of soft bending
actuators, using data-driven empirical models that lifts the need for complex analytical modelling and
material characterisation. The aim is to ultimately create a more controllable version of this class of soft
pneumatic actuators with embedded sensing capabilities, to act as compliant soft gripper fingers that can
be used in applications requiring both a ‘soft touch’ as well as more controllable object manipulation
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