Imitation and Mirror Systems in Robots through Deep Modality Blending Networks

Learning to interact with the environment not only empowers the agent with manipulation capability but also generates information to facilitate building of action understanding and imitation capabilities. This seems to be a strategy adopted by biological systems, in particular primates, as evidenced by the existence of mirror neurons that seem to be involved in multi-modal action understanding. How to benefit from the interaction experience of the robots to enable understanding actions and goals of other agents is still a challenging question. In this study, we propose a novel method, deep modality blending networks (DMBN), that creates a common latent space from multi-modal experience of a robot by blending multi-modal signals with a stochastic weighting mechanism. We show for the first time that deep learning, when combined with a novel modality blending scheme, can facilitate action recognition and produce structures to sustain anatomical and effect-based imitation capabilities. Our proposed system, can be conditioned on any desired sensory/motor value at any time-step, and can generate a complete multi-modal trajectory consistent with the desired conditioning in parallel avoiding accumulation of prediction errors. We further showed that given desired images from different perspectives, i.e. images generated by the observation of other robots placed on different sides of the table, our system could generate image and joint angle sequences that correspond to either anatomical or effect based imitation behavior. Overall, the proposed DMBN architecture not only serves as a computational model for sustaining mirror neuron-like capabilities, but also stands as a powerful machine learning architecture for high-dimensional multi-modal temporal data with robust retrieval capabilities operating with partial information in one or multiple modalities

DeepSym: Deep Symbol Generation and Rule Learning from Unsupervised Continuous Robot Interaction for Planning

Autonomous discovery of discrete symbols and rules from continuous interaction experience is a crucial building block of robot AI, but remains a challenging problem. Solving it will overcome the limitations in scalability, flexibility, and robustness of manually-designed symbols and rules, and will constitute a substantial advance towards autonomous robots that can learn and reason at abstract levels in open-ended environments. Towards this goal, we propose a novel and general method that finds action-grounded, discrete object and effect categories and builds probabilistic rules over them that can be used in complex action planning. Our robot interacts with single and multiple objects using a given action repertoire and observes the effects created in the environment. In order to form action-grounded object, effect, and relational categories, we employ a binarized bottleneck layer of a predictive, deep encoder-decoder network that takes as input the image of the scene and the action applied, and generates the resulting object displacements in the scene (action effects) in pixel coordinates. The binary latent vector represents a learned, action-driven categorization of objects. To distill the knowledge represented by the neural network into rules useful for symbolic reasoning, we train a decision tree to reproduce its decoder function. From its branches we extract probabilistic rules and represent them in PPDDL, allowing off-the-shelf planners to operate on the robot's sensorimotor experience. Our system is verified in a physics-based 3d simulation environment where a robot arm-hand system learned symbols that can be interpreted as 'rollable', 'insertable', 'larger-than' from its push and stack actions; and generated effective plans to achieve goals such as building towers from given cubes, balls, and cups using off-the-shelf probabilistic planners

Poly(ethylene glycol dimethacrylate-co-1-vinyl-1,2,4-triazole/ carbon nanotube, single-walled)/n-GaAs diode formed by surface polymerization

Poly(ethylene glycol dimethacrylate-co-1-vinyl-1,2,4-triazole/carbon nanotube, single-walled)/n-GaAs ([P(EGDMA-VTAZ)-CNSW]/n-GaAs) diode was fabricated by using surface polymerization method. Electrical properties were carried out at several temperatures. Dark current mechanisms were investigated by using current-voltage (I-V) measurements. It was shown that the fabricated structure exhibited rectification behaviour that makes it a good candidate for electronic device applications

How technology affected our privacy

After the recent spread of the Internet, many technology terms appeared like Smartphone, social media and cloud computing. Flexibility in use this technology encourage people to communicate and share their information in all kinds: photos, videos, documents and sometimes sensitive information like bank account. Annually we note the increased number of users of this technology to be arrived over billions of users. Most of these users are public which they do not realize ambiguity of technology and therefore easier to access their information. The abundance of information and poor knowledge of users about privacy led to the emergence of numerous threats and fears under this term. Thus, the importance of educating people about privacy issues and related risk factors become essential. This paper views the definition of privacy and what level of awareness should be applied. With try to understand some security issues and related laws. And compare between two different privacy laws

Temperature dependence of electrical characteristics of Cr/p-Si(100) Schottky barrier diodes

The electrical characteristics of Cr/p-Si(100) Schottky barrier diodes have been measured in the temperature range of 100-300 K. The I-V analysis based on thermionic emission (TE) theory has revealed an abnormal decrease of apparent barrier height and increase of ideality factor at low temperature. The conventional Richardson plot exhibits non-linearity below 200 K with the linear portion corresponding to activation energy 0.304 eV and Richardson constant (A*) value of 5.41 x 10(-3) Acm(-2)K(-2) is determined from the intercept at the ordinate of this experimental plot, which is much lower than the known value of 32 Acm(-2)K(-2) for p-type Si. It is demonstrated that these anomalies result due to the barrier height inhomogeneities prevailing at the metal-semiconductor interface. Hence, it has been concluded that the temperature dependence of the I-V characteristics of the Cr/p-Si Schottky barrier diode can be successfully explained on the basis of TE mechanism with a Gaussian distribution of the barrier heights. Furthermore, the value of the Richardson constant found is much closer than that obtained without considering the inhomogeneous barrier heights