Deep Recurrent Learning for Efficient Image Recognition Using Small Data

Recognition is fundamental yet open and challenging problem in computer vision. Recognition involves the detection and interpretation of complex shapes of objects or persons from previous encounters or knowledge. Biological systems are considered as the most powerful, robust and generalized recognition models. The recent success of learning based mathematical models known as artificial neural networks, especially deep neural networks, have propelled researchers to utilize such architectures for developing bio-inspired computational recognition models. However, the computational complexity of these models increases proportionally to the challenges posed by the recognition problem, and more importantly, these models require a large amount of data for successful learning. Additionally, the feedforward-based hierarchical models do not exploit another important biological learning paradigm, known as recurrency, which ubiquitously exists in the biological visual system and has been shown to be quite crucial for recognition. Consequently, this work aims to develop novel biologically relevant deep recurrent learning models for robust recognition using limited training data. First, we design an efficient deep simultaneous recurrent network (DSRN) architecture for solving several challenging image recognition tasks. The use of simultaneous recurrency in the proposed model improves the recognition performance and offers reduced computational complexity compared to the existing hierarchical deep learning models. Moreover, the DSRN architecture inherently learns meaningful representations of data during the training process which is essential to achieve superior recognition performance. However, probabilistic models such as deep generative models are particularly adept at learning representations directly from unlabeled input data. Accordingly, we show the generalization of the proposed deep simultaneous recurrency concept by developing a probabilistic deep simultaneous recurrent belief network (DSRBN) architecture which is more efficient in learning the underlying representation of the data compared to the state-of-the-art generative models. Finally, we propose a deep recurrent learning framework for solving the image recognition task using small data. We incorporate Bayesian statistics to the DSRBN generative model to propose a deep recurrent generative Bayesian model that addresses the challenge of learning from a small amount of data. Our findings suggest that the proposed deep recurrent Bayesian framework demonstrates better image recognition performance compared to the state-of-the-art models in a small data learning scenario. In conclusion, this dissertation proposes novel deep recurrent learning pipelines, which utilize not only limited training data to achieve improved image recognition performance but also require significantly reduced training parameters

Incorporating Human Expertise in Robot Motion Learning and Synthesis

With the exponential growth of robotics and the fast development of their advanced cognitive and motor capabilities, one can start to envision humans and robots jointly working together in unstructured environments. Yet, for that to be possible, robots need to be programmed for such types of complex scenarios, which demands significant domain knowledge in robotics and control. One viable approach to enable robots to acquire skills in a more flexible and efficient way is by giving them the capabilities of autonomously learn from human demonstrations and expertise through interaction. Such framework helps to make the creation of skills in robots more social and less demanding on programing and robotics expertise. Yet, current imitation learning approaches suffer from significant limitations, mainly about the flexibility and efficiency for representing, learning and reasoning about motor tasks. This thesis addresses this problem by exploring cost-function-based approaches to learning robot motion control, perception and the interplay between them. To begin with, the thesis proposes an efficient probabilistic algorithm to learn an impedance controller to accommodate motion contacts. The learning algorithm is able to incorporate important domain constraints, e.g., about force representation and decomposition, which are nontrivial to handle by standard techniques. Compliant handwriting motions are developed on an articulated robot arm and a multi-fingered hand. This work provides a flexible approach to learn robot motion conforming to both task and domain constraints. Furthermore, the thesis also contributes with techniques to learn from and reason about demonstrations with partial observability. The proposed approach combines inverse optimal control and ensemble methods, yielding a tractable learning of cost functions with latent variables. Two task priors are further incorporated. The first human kinematics prior results in a model which synthesizes rich and believable dynamical handwriting. The latter prior enforces dynamics on the latent variable and facilitates a real-time human intention cognition and an on-line motion adaptation in collaborative robot tasks. Finally, the thesis establishes a link between control and perception modalities. This work offers an analysis that bridges inverse optimal control and deep generative model, as well as a novel algorithm that learns cost features and embeds the modal coupling prior. This work contributes an end-to-end system for synthesizing arm joint motion from letter image pixels. The results highlight its robustness against noisy and out-of-sample sensory inputs. Overall, the proposed approach endows robots the potential to reason about diverse unstructured data, which is nowadays pervasive but hard to process for current imitation learning