Compact Q-Learning Optimized for Micro-robots with Processing and Memory Constraints

Scaling down robots to miniature size introduces many new challenges including memory and program size limitations, low processor performance and low power autonomy. In this paper we describe the concept and implementation of learning of a safewandering task with the autonomous micro-robots, Alice. We propose a simplified reinforcement learning algorithm based on one-step Qlearning that is optimized in speed and memory consumption. This algorithm uses only integer-based sum operators and avoids floatingpoint and multiplication operators. Finally, quality of learning is compared to a floating-point based algorithm

Memory and information processing in neuromorphic systems

A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. As Information and Communication Technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multi-neuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed neuromorphic computing platforms and system

A survey on real-time 3D scene reconstruction with SLAM methods in embedded systems

The 3D reconstruction of simultaneous localization and mapping (SLAM) is an important topic in the field for transport systems such as drones, service robots and mobile AR/VR devices. Compared to a point cloud representation, the 3D reconstruction based on meshes and voxels is particularly useful for high-level functions, like obstacle avoidance or interaction with the physical environment. This article reviews the implementation of a visual-based 3D scene reconstruction pipeline on resource-constrained hardware platforms. Real-time performances, memory management and low power consumption are critical for embedded systems. A conventional SLAM pipeline from sensors to 3D reconstruction is described, including the potential use of deep learning. The implementation of advanced functions with limited resources is detailed. Recent systems propose the embedded implementation of 3D reconstruction methods with different granularities. The trade-off between required accuracy and resource consumption for real-time localization and reconstruction is one of the open research questions identified and discussed in this paper

Behaviour design in microrobots:hierarchical reinforcement learning under resource constraints

In order to verify models of collective behaviors of animals, robots could be manipulated to implement the model and interact with real animals in a mixed-society. This thesis describes design of the behavioral hierarchy of a miniature robot, that is able to interact with cockroaches, and participates in their collective decision makings. The robots are controlled via a hierarchical behavior-based controller in which, more complex behaviors are built by combining simpler behaviors through fusion and arbitration mechanisms. The experiments in the mixed-society confirms the similarity between the collective patterns of the mixed-society and those of the real society. Moreover, the robots are able to induce new collective patterns by modulation of some behavioral parameters. Difficulties in the manual extraction of the behavioral hierarchy and inability to revise it, direct us to benefit from machine learning techniques, in order to devise the composition hierarchy and coordination in an automated way. We derive a Compact Q-Learning method for micro-robots with processing and memory constraints, and try to learn behavior coordination through it. The behavior composition part is still done manually. However, the problem of the curse of dimensionality makes incorporation of this kind of flat-learning techniques unsuitable. Even though optimizing them could temporarily speed up the learning process and widen their range of applications, their scalability to real world applications remains under question. In the next steps, we apply hierarchical learning techniques to automate both behavior coordination and composition parts. In some situations, many features of the state space might be irrelevant to what the robot currently learns. Abstracting these features and discovering the hierarchy among them can help the robot learn the behavioral hierarchy faster. We formalize the automatic state abstraction problem with different heuristics, and derive three new splitting criteria that adapt decision tree learning techniques to state abstraction. Proof of performance is supported by strong evidences from simulation results in deterministic and non-deterministic environments. Simulation results show encouraging enhancements in the required number of learning trials, robot's performance, size of the learned abstraction trees, and computation time of the algorithms. In the other hand, learning in a group provides free sources of knowledge that, if communicated, can broaden the scales of learning, both temporally and spatially. We present two approaches to combine output or structure of abstraction trees. The trees are stored in different RL robots in a multi-robot system, or in the trees learned by the same robot but using different methods. Simulation results in a non-deterministic football learning task provide strong evidences for enhancement in convergence rate and policy performance, specially in heterogeneous cooperations

Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision

Signal capture stands in the forefront to perceive and understand the environment and thus imaging plays the pivotal role in mobile vision. Recent explosive progresses in Artificial Intelligence (AI) have shown great potential to develop advanced mobile platforms with new imaging devices. Traditional imaging systems based on the "capturing images first and processing afterwards" mechanism cannot meet this unprecedented demand. Differently, Computational Imaging (CI) systems are designed to capture high-dimensional data in an encoded manner to provide more information for mobile vision systems.Thanks to AI, CI can now be used in real systems by integrating deep learning algorithms into the mobile vision platform to achieve the closed loop of intelligent acquisition, processing and decision making, thus leading to the next revolution of mobile vision.Starting from the history of mobile vision using digital cameras, this work first introduces the advances of CI in diverse applications and then conducts a comprehensive review of current research topics combining CI and AI. Motivated by the fact that most existing studies only loosely connect CI and AI (usually using AI to improve the performance of CI and only limited works have deeply connected them), in this work, we propose a framework to deeply integrate CI and AI by using the example of self-driving vehicles with high-speed communication, edge computing and traffic planning. Finally, we outlook the future of CI plus AI by investigating new materials, brain science and new computing techniques to shed light on new directions of mobile vision systems