Image resizing for achieving real-time in embedded AI

November 17, 2023 09:55 ,

Hu, Y., Liu, S., Abdelzaher, T. et al. Real-time task scheduling with image resizing for criticality-based machine perception, Real-Time Syst 58, 430\u2013455 (2022) DOI: 10.1007/s11241-022-09387-6.

This paper extends a previous conference publication that proposed a real-time task scheduling framework for criticality-based machine perception, leveraging image resizing as the tool to control the accuracy and execution time trade-off. Criticality-based machine perception reduces the computing demand of on-board AI-based machine inference pipelines (that run on embedded hardware) in applications such as autonomous drones and cars. By segmenting inputs, such as individual video frames, into smaller parts and allowing the downstream AI-based perception module to process some segments ahead of (or at a higher quality than) others, limited machine resources are spent more judiciously on more important parts of the input (e.g., on foreground objects in lieu of backgrounds). In recent work, we explored the use of image resizing as a way to offer a middle ground between full-resolution processing and dropping, thus allowing more flexibility in handling less important parts of the input. In this journal extension, we make the following contributions: (i) We relax a limiting assumption of our prior work; namely, the need for a \u201cperfect sensor” to identify which parts of the image are more critical. Instead, we investigate the use of real LiDAR measurements for quick-and-dirty image segmentation ahead of AI-based processing. (ii) We explore another dimension of freedom in the scheduler: namely, merging several nearby objects into a consolidated segment for downstream processing. We formulate the scheduling problem as an optimal resize-merge problem and design a solution for it. Experiments on an AI-powered embedded platform with a real-world driving dataset demonstrate the practicality and effectiveness of our proposed framework.

Posted in: Real-Time Systems , Tagged: , ,

Review of emotions in AI

November 17, 2023 09:51 ,

G. Assun��o, B. Patr�o, M. Castelo-Branco and P. Menezes, An Overview of Emotion in Artificial Intelligence, IEEE Transactions on Artificial Intelligence, vol. 3, no. 6, pp. 867-886, Dec. 2022 DOI: 10.1109/TAI.2022.3159614.

The field of artificial intelligence (AI) has gained immense traction over the past decade, producing increasingly successful applications as research strives to understand and exploit neural processing specifics. Nonetheless emotion, despite its demonstrated significance to reinforcement, social integration, and general development, remains a largely stigmatized and consequently disregarded topic by most engineers and computer scientists. In this article, we endorse emotion\u2019s value for the advancement of artificial cognitive processing, as well as explore real-world use cases of emotion-augmented AI. A schematization is provided on the psychological-neurophysiologic basics of emotion in order to bridge the interdisciplinary gap preventing emulation and integration in AI methodology, as well as exploitation by current systems. In addition, we overview three major subdomains of AI greatly benefiting from emotion, and produce a systematic survey of meaningful yet recent contributions to each area. To conclude, we address crucial challenges and promising research paths for the future of emotion in AI with the hope that more researchers will develop an interest for the topic and find it easier to develop their own contributions.

Posted in: Psycho-physiological bases of engineering , Tagged:

A brief summary of the state of the art in time series clustering

November 17, 2023 09:22 ,

Hailin Li, Zechen Liu, Xiaoji Wan, Time series clustering based on complex network with synchronous matching states, Expert Systems with Applications, Volume 211, 2023 DOI: 10.1016/j.eswa.2022.118543.

Due to the extensive existence of time series in various fields, more and more research on time series data mining, especially time series clustering, has been done in recent years. Clustering technology can extract valuable information and potential patterns from time series data. This paper proposes a time series Clustering method based on Synchronous matching of Complex networks (CSC). This method uses density peak clustering algorithm to identify the state of each time point and obtains the state sequence according to the timeline of the original time series. State sequences is a new method to represent time series. By comparing two state sequences synchronously, the length of state sequence with step is calculated and the similarity is presented, which forms a new method to calculate the similarity of time series. Based on the obtained time series similarity, the relationship network of time series is constructed. Simultaneously, the community discovery technology is applied to cluster the relationship network and further achieve the complete time series clustering. The detailed process and simulation experiments of CSC method are given. Experimental results on different datasets show that CSC method is superior to other traditional time series clustering methods.

Posted in: Probability and statistics , Tagged:

More robust KF through the use of skewed distributions

November 10, 2023 10:24 ,

M. Bai, Y. Huang, B. Chen and Y. Zhang, A Novel Robust Kalman Filtering Framework Based on Normal-Skew Mixture Distribution, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 11, pp. 6789-6805, Nov. 2022 DOI: 10.1109/TSMC.2021.3098299.

In this article, a novel normal-skew mixture (NSM) distribution is presented to model the normal and/or heavy-tailed and/or skew nonstationary distributed noises. The NSM distribution can be formulated as a hierarchically Gaussian presentation by leveraging a Bernoulli distributed random variable. Based on this, a novel robust Kalman filtering framework can be developed utilizing the variational Bayesian method, where the one-step prediction and measurement-likelihood densities are modeled as NSM distributions. For implementation, several exemplary robust Kalman filters (KFs) are derived based on some specific cases of NSM distribution. The relationships between some existing robust KFs and the presented framework are also revealed. The superiority of the proposed robust Kalman filtering framework is validated by a target tracking simulation example.

Posted in: Bayesian filtering , Tagged: , ,

Using CNNs trained with image data to predict time series data

November 10, 2023 10:13 ,

Aniello De Santo, Antonino Ferraro, Antonio Galli, Vincenzo Moscato, Giancarlo Sperl�, Evaluating time series encoding techniques for Predictive Maintenance, Expert Systems with Applications, Volume 210, 2022 DOI: 10.1016/j.eswa.2022.118435.

Predictive Maintenance has become an important component in modern industrial scenarios, as a way to minimize down-times and fault rate for different equipment. In this sense, while machine learning and deep learning approaches are promising due to their accurate predictive abilities, their data-heavy requirements make them significantly limited in real world applications. Since one of the main issues to overcome is lack of consistent training data, recent work has explored the possibility of adapting well-known deep-learning models for image recognition, by exploiting techniques to encode time series as images. In this paper, we propose a framework for evaluating some of the best known time series encoding techniques, together with Convolutional Neural Network-based image classifiers applied to predictive maintenance tasks. We conduct an extensive empirical evaluation of these approaches for the failure prediction task on two real-world datasets (PAKDD2020 Alibaba AI OPS Competition and NASA bearings), also comparing their performances with respect to the state-of-the-art approaches. We further discuss advantages and limitation of the exploited models when coupled with proper data augmentation techniques.

Posted in: Uncategorized , Tagged: ,

Transfering RL knowledge to other tasks by transferring components of RL

November 10, 2023 09:49 ,

Rex G. Liu, Michael J. Frank, Hierarchical clustering optimizes the tradeoff between compositionality and expressivity of task structures for flexible reinforcement learning, Artificial Intelligence, Volume 312, 2022 DOI: 10.1016/j.artint.2022.103770.

A hallmark of human intelligence, but challenging for reinforcement learning (RL) agents, is the ability to compositionally generalise, that is, to recompose familiar knowledge components in novel ways to solve new problems. For instance, when navigating in a city, one needs to know the location of the destination and how to operate a vehicle to get there, whether it be pedalling a bike or operating a car. In RL, these correspond to the reward function and transition function, respectively. To compositionally generalize, these two components need to be transferable independently of each other: multiple modes of transport can reach the same goal, and any given mode can be used to reach multiple destinations. Yet there are also instances where it can be helpful to learn and transfer entire structures, jointly representing goals and transitions, particularly whenever these recur in natural tasks (e.g., given a suggestion to get ice cream, one might prefer to bike, even in new towns). Prior theoretical work has explored how, in model-based RL, agents can learn and generalize task components (transition and reward functions). But a satisfactory account for how a single agent can simultaneously satisfy the two competing demands is still lacking. Here, we propose a hierarchical RL agent that learns and transfers individual task components as well as entire structures (particular compositions of components) by inferring both through a non-parametric Bayesian model of the task. It maintains a factorised representation of task components through a hierarchical Dirichlet process, but it also represents different possible covariances between these components through a standard Dirichlet process. We validate our approach on a variety of navigation tasks covering a wide range of statistical correlations between task components and show that it can also improve generalisation and transfer in more complex, hierarchical tasks with goal/subgoal structures. Finally, we end with a discussion of our work including how this clustering algorithm could conceivably be implemented by cortico-striatal gating circuits in the brain.

Posted in: Uncategorized , Tagged: , ,

A RRT-based method that addresses combined task and motion planning

November 10, 2023 09:38 ,

Riccardo Caccavale, Alberto Finzi, A rapidly-exploring random trees approach to combined task and motion planning, Robotics and Autonomous Systems, Volume 157, 2022 DOI: 10.1016/j.robot.2022.104238.

Task and motion planning in robotics are typically addressed by separated intertwined methods. Task planners generate abstract high-level actions to be executed, while motion planners provide the associated discrete movements in the configuration space satisfying kinodynamic constraints. However, these two planning processes are strictly dependent, therefore the problem of combining task and motion planning with a uniform approach is very relevant. In this work, we tackle this issue by proposing a RRT-based method that addresses combined task and motion planning. Our approach relies on a combined metric space where both symbolic (task) and sub-symbolic (motion) spaces are represented. The associated notion of distance is then exploited by a RRT-based planner to generate a plan that includes both symbolic actions and feasible movements in the configuration space. The proposed method is assessed in several case studies provided by a real-world hospital logistic scenario, where an omni-directional mobile robot is involved in navigation and transportation tasks.

Posted in: Robot motion planning, Robot task planning , Tagged:

Using results from belief-based planning for Bayesian inference in robotics

November 10, 2023 09:35 ,

Farhi, E.I., Indelman, V., Bayesian incremental inference update by re-using calculations from belief space planning: a new paradigm, Auton Robot 46, 783\u2013816 (2022). DOI: 10.1007/s10514-022-10045-w.

Inference and decision making under uncertainty are key processes in every autonomous system and numerous robotic problems. In recent years, the similarities between inference and decision making triggered much work, from developing unified computational frameworks to pondering about the duality between the two. In spite of these efforts, inference and control, as well as inference and belief space planning (BSP) are still treated as two separate processes. In this paper we propose a paradigm shift, a novel approach which deviates from conventional Bayesian inference and utilizes the similarities between inference and BSP. We make the key observation that inference can be efficiently updated using predictions made during the decision making stage, even in light of inconsistent data association between the two. We developed a two staged process that implements our novel approach and updates inference using calculations from the precursory planning phase. Using autonomous navigation in an unknown environment along with iSAM2 efficient methodologies as a test case, we benchmarked our novel approach against standard Bayesian inference, both with synthetic and real-world data (KITTI dataset). Results indicate that not only our approach improves running time by at least a factor of two while providing the same estimation accuracy, but it also alleviates the computational burden of state dimensionality and loop closures.

Posted in: Mobile Robot Localization, Mobile robot mapping , Tagged: ,

Real-time and Bayesian-enabled ICP for mobile robot localization and mapping in a Bayesian framework

November 10, 2023 09:31 ,

Maken FA, Ramos F, Ott L. , Bayesian iterative closest point for mobile robot localization, The International Journal of Robotics Research. 2022;41(9-10):851-874 DOI: 10.1177/02783649221101417.

Accurate localization of a robot in a known environment is a fundamental capability for successfully performing path planning, manipulation, and grasping tasks. Particle filters, also known as Monte Carlo localization (MCL), are a commonly used method to determine the robot\u2019s pose within its environment. For ground robots, noisy wheel odometry readings are typically used as a motion model to predict the vehicle\u2019s location. Such a motion model requires tuning of various parameters based on terrain and robot type. However, such an ego-motion estimation is not always available for all platforms. Scan matching using the iterative closest point (ICP) algorithm is a popular alternative approach, providing ego-motion estimates for localization. Iterative closest point computes a point estimate of the transformation between two poses given point clouds captured at these locations. Being a point estimate method, ICP does not deal with the uncertainties in the scan alignment process, which may arise due to sensor noise, partial overlap, or the existence of multiple solutions. Another challenge for ICP is the high computational cost required to align two large point clouds, limiting its applicability to less dynamic problems. In this paper, we address these challenges by leveraging recent advances in probabilistic inference. Specifically, we first address the run-time issue and propose SGD-ICP, which employs stochastic gradient descent (SGD) to solve the optimization problem of ICP. Next, we leverage SGD-ICP to obtain a distribution over transformations and propose a Markov Chain Monte Carlo method using stochastic gradient Langevin dynamics (SGLD) updates. Our ICP variant, termed Bayesian-ICP, is a full Bayesian solution to the problem. To demonstrate the benefits of Bayesian-ICP for mobile robotic applications, we propose an adaptive motion model employing Bayesian-ICP to produce proposal distributions for Monte Carlo Localization. Experiments using both Kinect and 3D LiDAR data show that our proposed SGD-ICP method achieves the same solution quality as standard ICP while being significantly more efficient. We then demonstrate empirically that Bayesian-ICP can produce accurate distributions over pose transformations and is fast enough for online applications. Finally, using Bayesian-ICP as a motion model alleviates the need to tune the motion model parameters from odometry, resulting in better-calibrated localization uncertainty.

Posted in: Mobile Robot Localization , Tagged: ,

Adaptation of industrial robots to variations in tasks through RL

November 10, 2023 09:27 ,

Tian Yu, Qing Chang, User-guided motion planning with reinforcement learning for human-robot collaboration in smart manufacturing, Expert Systems with Applications, Volume 209, 2022 DOI: 10.1016/j.eswa.2022.118291.

In today\u2019s manufacturing system, robots are expected to perform increasingly complex manipulation tasks in collaboration with humans. However, current industrial robots are still largely preprogrammed with very little autonomy and still required to be reprogramed by robotics experts for even slightly changed tasks. Therefore, it is highly desirable that robots can adapt to certain task changes with motion planning strategies to easily work with non-robotic experts in manufacturing environments. In this paper, we propose a user-guided motion planning algorithm in combination with reinforcement learning (RL) method to enable robots automatically generate their motion plans for new tasks by learning from a few kinesthetic human demonstrations. Features of common human demonstrated tasks in a specific application environment, e.g., desk assembly or warehouse loading/unloading are abstracted and saved in a library. The definition of semantical similarity between features in the library and features of a new task is proposed and further used to construct the reward function in RL. To achieve an adaptive motion plan facing task changes or new task requirements, features embedded in the library are mapped to appropriate task segments based on the trained motion planning policy using Q-learning. A new task can be either learned as a combination of a few features in the library or a requirement for further human demonstration if the current library is insufficient for the new task. We evaluate our approach on a 6 DOF UR5e robot on multiple tasks and scenarios and show the effectiveness of our method with respect to different scenarios.

Posted in: Applications of reinforcement learning to robots , Tagged: , ,