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检索条件"任意字段=International Conference on Methods and Models in Automation and Robotics"

共 24622 条记录，以下是411-420 订阅

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Navigating the Unknown: Uncertainty-Aware Compute-in-Memory Autonomy of Edge robotics

Navigating the Unknown: Uncertainty-Aware Compute-in-Memory ...

引用

27th Design, automation and Test in Europe conference and Exhibition (DATE)

作者： Darabi, Nastaran Shukla, Priyesh Jayasuriya, Dinithi Kumar, Divake Stutts, Alex C. Trivedi, Amit Ranjan UIC AEON Lab Chicago IL 60607 USA

ISBN: (纸本)9798350348606;9783981926385

This paper addresses the challenging problem of energy-efficient and uncertainty-aware pose estimation in insect-scale drones, which is crucial for tasks such as surveillance in constricted spaces and for enabling non-intrusive spatial intelligence in smart homes. Since tiny drones operate in highly dynamic environments, where factors like lighting and human movement impact their predictive accuracy, it is crucial to deploy uncertainty-aware prediction algorithms that can account for environmental variations and express not only the prediction but also confidence in the prediction. We address both of these challenges with Compute-in-Memory (CIM) which has become a pivotal technology for deep learning acceleration at the edge. While traditional CIM techniques are promising for energy-efficient deep learning, to bring in the robustness of uncertainty-aware predictions at the edge, we introduce a suite of novel techniques: First, we discuss CIM-based acceleration of Bayesian filtering methods uniquely by leveraging the Gaussian-like switching current of CMOS inverters along with co-design of kernel functions to operate with extreme parallelism and with extreme energy efficiency. Secondly, we discuss the CIM-based acceleration of variational inference of deep learning models through probabilistic processing while unfolding iterative computations of the method with a compute reuse strategy to significantly minimize the workload. Overall, our co-design methodologies demonstrate the potential of CIM to improve the processing efficiency of uncertainty-aware algorithms by orders of magnitude, thereby enabling edge robotics to access the robustness of sophisticated prediction frameworks within their extremely stringent area/power resources.

关键词： Compute-in-memory Edge robotics Autonomous Navigation Visual Odometry Particle Filtering

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Indoor Multi-Anchor Collaborative Localization for Unmanned Systems

Indoor Multi-Anchor Collaborative Localization for Unmanned ...

引用

2024 IEEE international conference on robotics and Biomimetics, ROBIO 2024

作者： Tai, Beijia Wei, Yangjie Zhang, Guangyu He, Yuqing Cui, Siyao Ministry of Education College of Computer Science and Engineering Northeastern University Key Laboratory of Intelligent Computing in Medical Image Wenhua Street 3 Shenyang110819 China Shenyang Institute of Automation Chinese Academy of Science Shenyang China

ISBN: (纸本)9781665481090

Accurate tracking of unmanned devices is essen-tial for higher-level decision-making in collaborative confrontation scenarios. However, the robustness of current positioning methods based on Ultra-Wideband (UWB) is limit, and the positioning error is unstable. This paper presents a collabo-rative localization algorithm specifically designed for indoor unmanned systems, which leverages UWB technology combined with Least Squares (LS) and Extended Kalman Filter (EKF) models to achieve anchor calibration, as well as precise and stable positioning of unmanned devices. First, the positions of anchors is estimated using fixed-point ranging information;Second, the positions of anchors are simultaneously corrected while locating the tags on unmanned devices. The experimental results show that our algorithm achieves positioning accuracy within 10 cm in the three-dimensional space, even with a ranging error of 5 cm. © 2024 IEEE.

关键词： Ultra-wideband (UWB)

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Utilizing Large Language models for Robot Skill Reward Shaping in Reinforcement Learning 17th

Utilizing Large Language Models for Robot Skill Reward Shapi...

引用

17th international conference on Intelligent robotics and Applications

作者： Guo, Qi Liu, Xing Hui, Jianjiang Liu, Zhengxiong Huang, Panfeng Northwestern Polytech Univ Res Ctr Intelligent Robot Sch Astronaut Xian 710072 Peoples R China Beijing Inst Tracking & Telecommun Technol Beijing 100094 Peoples R China

ISBN: (纸本)9789819607822;9789819607839

In this paper, we examines the integration of LLMs in designing reward functions for reinforcement learning (RL) to enhance robotic applications with minimal human input. In RL, the reward function is pivotal, guiding the agent's learning trajectory by evaluating the desirability of behaviors within specific environments. Traditional reward functions, often sparse, lead to slow convergence as agents require extensive interactions to learn effectively. By leveraging LLM's ability to generate code from task semantics, we propose a new method that reduces the complexity of reward design, allowing even non-experts to create effective reward policies using semantic prompts. We utilize the Soft ActorCritic (SAC) algorithm, known for its efficiency and stability, to train agents under these conditions. To validate the efficacy of our method, we compare it with traditional techniques like Trajectory-ranked reward extrapolation (T-REX). Our findings indicate that the LLM-generated rewards enable quicker convergence and are as effective as those crafted through conventional methods, demonstrating the potential of LLMs to revolutionize reward shaping in RL. Furthermore, we transferred the robot door-opening task from the real-world simulation environment to a real robot, achieving *** approach allows for the rapid deployment of robotic systems, making sophisticated robotics technology more accessible and feasible for a wider range of applications. This study underscores the transformative impact of integrating advanced language models into the realm of robotics and RL, opening up new avenues for future research and application.

关键词： Large language model(LLMs) reward shaping reinforcement learning

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Automatic Acquisition of a Repertoire of Diverse Grasping Trajectories through Behavior Shaping and Novelty Search 39

Automatic Acquisition of a Repertoire of Diverse Grasping Tr...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Morel, Aurelien Kunimoto, Yakumo Coninx, Alex Doncieux, Stephane Sorbonne Univ CNRS Inst Syst Intelligents & Robot ISIR F-75005 Paris France Ecole Polytech Fed Lausanne CH-1015 Lausanne Switzerland

ISBN: (纸本)9781728196817

Grasping a particular object may require a dedicated grasping movement that may also be specific to the robot end-effector. No generic and autonomous method does exist to generate these movements without making hypotheses on the robot or on the object. Learning methods could help to autonomously discover relevant grasping movements, but they face an important issue: grasping movements are so rare that a learning method based on exploration has little chance to ever observe an interesting movement, thus creating a bootstrap issue. We introduce an approach to generate diverse grasping movements in order to solve this problem. The movements are generated in simulation, for particular object positions. We test it on several simulated robots: Baxter, Pepper and a Kuka Iiwa arm. Although we show that generated movements actually work on a real Baxter robot, the aim is to use this method to create a large dataset to bootstrap deep learning methods.

关键词： Learning systems Training Simulation Grasping Diversity methods Predictive models Behavioral sciences

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Brick Yourself within 3 Minutes 39

Brick Yourself within 3 Minutes

引用

IEEE international conference on robotics and automation (ICRA)

作者： Zhou, Guyue Luo, Liyi Xu, Hao Zhang, Xinliang Guo, Haole Zhao, Hao Tsinghua Univ Inst Ind Res AIR Beijing Peoples R China McGill Univ Montreal PQ H3A 2T5 Canada Qianzhi Technol Shanghai Peoples R China Intel Labs Beijing Peoples R China Peking Univ Beijing Peoples R China

ISBN: (纸本)9781728196817

This paper presents an intelligent machine which can automatically convert the captured portrait into a physical gadget made up of LEGO bricks. On the contrary to synthesising a 2D image or a virtual 3D object, generating physical 3D assembly object needs to take physical properties and assembly process into consideration, leading to more challenges. To generate brick models for arbitrary portraits, we formulate the transformation between the attribute space (extracted from 2D images) and the brick model space as a constraint integer programming problem which can be solved with a heuristic search method. Furthermore, as the bricks are physically scattered, we propose an algorithm to generate corresponding assembly instructions for customized figure-featured-bricks to facilitate users' assembly. Meanwhile, we deploy the proposed algorithms on an automatic machine which integrates a camera, a printer, a laptop, and a brick operation unit. Finally, the generated brick models and assembly instructions are evaluated by a large number of users. It is worth noting that the whole system works as an intelligent vending machine, producing a 150-brick-model within 3 minutes.

关键词： Integer programming Three-dimensional displays Portable computers automation Search methods Supply chains Prototypes

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Using Eye Gaze to Forecast Human Pose in Everyday Pick and Place Actions 39

Using Eye Gaze to Forecast Human Pose in Everyday Pick and P...

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IEEE international conference on robotics and automation (ICRA)

作者： Razali, Haziq Demiris, Yiannis Imperial Coll London Dept EEE Personal Robot Lab London SW7 2AZ England

ISBN: (纸本)9781728196817

Collaborative robots that operate alongside humans require the ability to understand their intent and forecast their pose. Among the various indicators of intent, the eye gaze is particularly important as it signals action towards the gazed object. By observing a person's gaze, one can effectively predict the object of interest and subsequently, forecast the person's pose. We leverage this and present a method that forecasts the human pose using gaze information for everyday pick and place actions in a home environment. Our method first attends to fixations to locate the coordinates of the object of interest before inputting said coordinates to a pose forecasting network. Experiments on the MoGaze dataset show that our gaze network lowers the errors of existing pose forecasting methods and that incorporating prior in the form of textual instructions further lowers the errors by a significant amount. Furthermore, the use of eye gaze now allows a simple multilayer perceptron network to directly forecast the keypose.

关键词： Knowledge engineering Robot kinematics Brightness Collaboration Color Predictive models Multilayer perceptrons

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Investigating the Generalizability of Assistive Robots models over Various Tasks 21

Investigating the Generalizability of Assistive Robots Model...

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21st international conference on Ubiquitous Robots (UR)

作者： Osooli, Hamid Coco, Christopher Spanos, Johnathan Majdi, Amin Azadeh, Reza Univ Massachusetts Lowell Persistent Auton & Robot Learning PeARL Lab Lowell MA 01854 USA

ISBN: (纸本)9798350361087;9798350361070

In the domain of assistive robotics, the significance of effective modeling is well acknowledged. Prior research has primarily focused on enhancing model accuracy or involved the collection of extensive, often impractical amounts of data. While improving individual model accuracy is beneficial, it necessitates constant remodeling for each new task and user interaction. In this paper, we investigate the generalizability of different modeling methods. We focus on constructing the dynamic model of an assistive exoskeleton using six data-driven regression algorithms. Six tasks are considered in our experiments, including horizontal, vertical, diagonal from left leg to the right eye and the opposite, as well as eating and pushing. We constructed thirty-six unique models applying different regression methods to data gathered from each task. Each trained model's performance was evaluated in a cross-validation scenario, utilizing five folds for each dataset. These trained models are then tested on the other tasks that the model is not trained with. Finally the models in our study are assessed in terms of generalizability. Results show the superior generalizability of the task model performed along the horizontal plane, and decision tree based algorithms.

关键词： Exoskeleton (robotics)

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Keypoint-Based Category-Level Object Pose Tracking from an RGB Sequence with Uncertainty Estimation 39

Keypoint-Based Category-Level Object Pose Tracking from an R...

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IEEE international conference on robotics and automation (ICRA)

作者： Lin, Yunzhi Tremblay, Jonathan Tyree, Stephen Vela, Patricio A. Birchfield, Stan NVIDIA Santa Clara CA 95051 USA Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)9781728196817

We propose a single-stage, category-level 6-DoF pose estimation algorithm that simultaneously detects and tracks instances of objects within a known category. Our method takes as input the previous and current frame from a monocular RGB video, as well as predictions from the previous frame, to predict the bounding cuboid and 6-DoF pose (up to scale). Internally, a deep network predicts distributions over object keypoints (vertices of the bounding cuboid) in image coordinates, after which a novel probabilistic filtering process integrates across estimates before computing the final pose using PnP. Our framework allows the system to take previous uncertainties into consideration when predicting the current frame, resulting in predictions that are more accurate and stable than single frame methods. Extensive experiments show that our method outperforms existing approaches on the challenging Objectron benchmark of annotated object videos. We also demonstrate the usability of our work in an augmented reality setting.

关键词： Heating systems Uncertainty Filtering Pose estimation Benchmark testing Predictive models Probabilistic logic

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Research Towards an Optimal Method of Modeling and Regulating a Cement Mill Using AI Algorithms 28th

Research Towards an Optimal Method of Modeling and Regulatin...

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2024 international conference automation

作者： Pawus, Dawid Paszkiel, Szczepan Opole Univ Technol Fac Elect Engn Automat Control & Informat Proszkowska St 76 PL-45758 Opole Poland

ISBN: (纸本)9783031782657;9783031782664

This research explores the optimization of modeling and control strategies for a cement mill using advanced Artificial Intelligence (AI) techniques. Neural Nonlinear AutoRegressive with eXogenous input (NNARX) models are employed to capture the nonlinear, multivariable dynamics of the cement grinding process. The Levenberg-Marquardt algorithm demonstrated superior training performance, achieving high accuracy in predicting key process parameters like inlet chamber fill and main drive current. The study also presents a neural control approach based on inverse modeling, enabling precise input predictions to maintain desired output conditions, stabilizing product quality and minimizing energy usage. Comparisons of neural network configurations suggest that simpler models are effective, with only slight benefits from more complex networks.

关键词： non-linear models artificial neural networks expert system NARX comparative study cement mill process control practical approach

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Bayesian Optimization Meets Hybrid Zero Dynamics: Safe Parameter Learning for Bipedal Locomotion Control 39

Bayesian Optimization Meets Hybrid Zero Dynamics: Safe Param...

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IEEE international conference on robotics and automation (ICRA)

作者： Yang, Lizhi Li, Zhongyu Zeng, Jun Sreenath, Koushil Univ Calif Berkeley Dept Mech Engn Hybrid Robot Grp Berkeley CA 94720 USA

ISBN: (纸本)9781728196817

In this paper, we propose a multi-domain control parameter learning framework that combines Bayesian Optimization (BO) and Hybrid Zero Dynamics (HZD) for locomotion control of bipedal robots. We leverage BO to learn the control parameters used in the HZD-based controller. The learning process is firstly deployed in simulation to optimize different control parameters for a large repertoire of gaits. Next, to tackle the discrepancy between the simulation and the real world, the learning process is applied on the physical robot to learn for corrections to the control parameters learned in simulation while also respecting a safety constraint for gait stability. This method empowers an efficient sim-to-real transition with a small number of samples in the real world, and does not require a valid controller to initialize the training in simulation. Our proposed learning framework is experimentally deployed and validated on a bipedal robot Cassie to perform versatile locomotion skills with improved performance on smoothness of walking gaits and reduction of steady-state tracking errors.

关键词： Legged locomotion Process control Stability analysis Steady-state Safety Numerical models Bayes methods

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