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arXiv 2024年

作者： Guo, Haoran Wang, Haoyang Li, Zhengxiong Bai, He Tao, Lingfeng Oklahoma State University Mechatronics and Intelligent Robotics Lab 563 Engineering North StillwaterOK74075 United States University of Colorado Denver Department of Computer Science and Engineering 1380 Lawrence St. Center LW-834 DenverCO80217 United States

Tactile information effectively enables faster training and better task performance for learning-based in-hand manipulation. Existing approaches are validated in simulated environments with a large number of tactile sensors. However, attaching such sensors to a real robot hand is not applicable due to high cost and physical limitations. To enable real-world adoption of tactile sensors, this study investigates the impact of tactile sensors, including their varying quantities and placements on robot hands, on the dexterous manipulation task performance and analyzes the importance of each. Through empirically decreasing the sensor quantities, we successfully find an optimized set of tactile sensors (21 sensors) configuration, which keeps over 93% task performance with only 20% sensor quantities compared to the original set (92 sensors) for the block manipulation task, leading to a potential reduction of over 80% in sensor manufacturing and design costs. To transform the empirical results into a generalizable understanding, we build a task performance prediction model with a weighted linear regression algorithm and use it to forecast the task performance with different sensor configurations. To show its generalizability, we verified this model in egg and pen manipulation tasks and achieved an average prediction error of 3.12%. Copyright © 2024, The Authors. All rights reserved.

关键词： Robotic arms

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iMTSP: Solving Min-Max Multiple Traveling Salesman Problem with Imperative Learning

arXiv

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arXiv 2024年

作者： Guo, Yifan Ren, Zhongqiang Wang, Chen The Flight Dynamics & Control/Hybrid Systems Lab Purdue University West LafayetteIN47907 United States Robotics Institute Carnegie Mellon University PittsburghPA15213 United States Lab Department of Computer Science and Engineering University at Buffalo NY14260 United States

This paper considers a Min-Max Multiple Traveling Salesman Problem (MTSP), where the goal is to find a set of tours, one for each agent, to collectively visit all the cities while minimizing the length of the longest tour. Though MTSP has been widely studied, obtaining near-optimal solutions for large-scale problems is still challenging due to its NP-hardness. Recent efforts in data-driven methods face challenges of the need for hard-to-obtain supervision and issues with high variance in gradient estimations, leading to slow convergence and highly sub-optimal solutions. We address these issues by reformulating MTSP as a bilevel optimization problem, using the concept of imperative learning (IL). This involves introducing an allocation network that decomposes the MTSP into multiple single-agent traveling salesman problems (TSPs). The longest tour from these TSP solutions is then used to self-supervise the allocation network, resulting in a new self-supervised, bilevel, end-to-end learning framework, which we refer to as imperative MTSP (iMTSP). Additionally, to tackle the high-variance gradient issues during the optimization, we introduce a control variate-based gradient estimation algorithm. Our experiments showed that these innovative designs enable our gradient estimator to converge 20× faster than the advanced reinforcement learning baseline, and find up to 80% shorter tour length compared with Google OR-Tools MTSP solver, especially in large-scale problems (e.g. 1000 cities and 15 agents). © 2024, CC BY.

关键词： Traveling salesman problem

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Augmenting a Description Logic with Probability for Motion Patterns Within QSTR 6th

Augmenting a Description Logic with Probability for Motion ...

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6th International Conference on Advanced Computing, Networking, and Informatics, ICACNI 2018

作者： Talukdar, Upasana Barua, Rupam Hazarika, Shyamanta M. Biomimetic & Cognitive Robotics Lab Department of Computer Science & Engineering Tezpur University Tezpur India Department of Computer Science & Engineering Jorhat Engineering College Jorhat India Mechatronics and Robotics Lab Department of Mechanical Engineering Indian Institute of Technology Guwahati India

ISBN: (纸本)9789811396793

Motion patterns are spatiotemporal. Human everyday spatiotemporal reasoning is predominantly qualitative;hence, a qualitative abstraction of motion patterns holds promise. Reasoning over qualitative models of spatiotemporal change is referred to as qualitative spatiotemporal reasoning (QSTR). Recognition of patterns within a QSTR framework involves reasoning over a multitude of entities. These entities interacting in numerous ways often lead to hierarchies and ample uncertainties. Herein lies the motivation of using a formal basis for the representation of large and diverse knowledge. Description logics (DLs), a family of knowledge representation formalism providing object-oriented representation with formal semantics, are a natural choice. However, traditional DLs delineate a delicate approach to handle uncertain features and relationships. A paradigm that combines a DL formalization with probability theory is required. Bayesian Networks (BNs) are the most widely used models to represent knowledge about an uncertain domain. This paper combines a DL with BN and proposes a formal, explicit qualitative knowledge representation formalism for recognition of motion patterns within an uncertain domain. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Bayesian networks

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Estimating Complexity for Perception-based ADAS in Unstructured Road Environments

Estimating Complexity for Perception-based ADAS in Unstructu...

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IEEE Symposium on Intelligent Vehicle

作者： Imane Taourarti Ayesha Choudhary Vivek Kumar Paswan Aditya Kumar Arunkumar Ramaswamy Javier Ibanez-Guzman Bruno Monsuez Adriana Tapus Autonomous Systems and Robotics Lab/U2IS Computer Science and System Engineering (U2IS) ENSTA Paris Institut Polytechnique de Paris France Research Department Renault Group France School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi India

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Advanced Driver Assistance Systems (ADAS) are rapidly becoming a standard feature in modern road vehicles, enhancing safety and driver comfort. As ADAS adoption expands across diverse geographical and cultural regions, the performance of camera-based perception systems may vary significantly due to environmental and expected social behaviour of the different actors. This paper explores the referred factors and evaluates the traffic environment complexity for vehicles with different levels of automation. In particular, we propose a novel modeling and quantitative assessment approach for environment complexity. Specifically, we compare a perception model trained on United States dataset with a dataset from India, a nation characterized by unique traffic patterns, signage conventions, and cultural norms to assess its performance variation, and to lay the basis for proposing influencing factors of traffic environment complexity. We establish a scheme of referential and additional static factors and based on an expert evaluation, environment complexity is established. The effectiveness of the proposed approach is testified by naturalistic driving data. These findings pave the way for future research in intelligent driving and emphasize the importance of addressing cultural nuances as vehicle automation levels increase.

关键词： Measurement Automation Roads Road vehicles Globalization Complexity theory Safety

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A Miniaturised Camera-based Multi-Modal Tactile Sensor

A Miniaturised Camera-based Multi-Modal Tactile Sensor

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IEEE International Conference on robotics and Automation (ICRA)

作者： Kaspar Althoefer Yonggen Ling Wanlin Li Xinyuan Qian Wang Wei Lee Peng Qi The Centre for Advanced Robotics @ Queen Mary (ARQ) Queen Mary University of London London United Kingdom Tencent Robotics X Lab Shenzhen China Beijing Institute for General Artificial Intelligence (BIGAI) Beijing China The Department of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai China

In conjunction with huge recent progress in cam-era and computer vision technology, camera-based sensors have increasingly shown considerable promise in relation to tactile sensing. In comparison to competing technologies (be they resistive, capacitive or magnetic based), they offer super-high-resolution, while suffering from fewer wiring problems. The human tactile system is composed of various types of mechanoreceptors, each able to perceive and process distinct information such as force, pressure, texture, etc. Camera-based tactile sensors such as GelSight mainly focus on high-resolution geometric sensing on a flat surface, and their force measurement capabilities are limited by the hysteresis and non-linearity of the silicone material. In this paper, we present a miniaturised dome-shaped camera-based tactile sensor that allows accurate force and tactile sensing in a single coherent system. The key novelty of the sensor design is as follows. First, we demonstrate how to build a smooth silicone hemispheric sensing medium with uniform markers on its curved surface. Second, we enhance the illumination of the rounded silicone with diffused LEDs. Third, we construct a force-sensitive mechanical structure in a compact form factor with usage of springs to accurately perceive forces. Our multi-modal sensor is able to acquire tactile information from multi-axis forces, local force distribution, and contact geometry, all in real-time. We apply an end-to-end deep learning method to process all the information.

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Study on the Impact of Situational Explanations and Prior Information Given to Users on Trust and Perceived Intelligence in Autonomous Driving in a Video-based 2x2 Design

Study on the Impact of Situational Explanations and Prior In...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Kolja Kühnlenz Barbara Kühnlenz Department of Electrical Engineering and Computer Science Robotics Research Lab Coburg University of Applied Sciences and Arts Coburg Germany Academic Center for Sciences and Humanities Coburg University of Applied Sciences and Arts Coburg Germany

In this paper, results from a video-based study on the influence of prior information given to users and explanations situationally given by the vehicle itself on trust and perceived intelligence are presented using a simulated autonomous vehicle in an ambiguous driving situation. A 2x2 between-subjects design is chosen with two independent variables ‘prior information’ (extended/short) and ‘explanations’ (yes/no) with users pseudo-randomly assigned to one of the four conditions. Significant results from 189 test persons reveal, that trust depends on how the capabilities of the intelligent vehicle are explained a priori and not on situational explanations, while perceived intelligence is influenced by both variables. Additional interactions of prior information and user gender is noted with respect to perceived intelligence. As one side effect, it is found, that male users felt significantly more safe than female users with also higher ratings of intention to use the vehicle independently of given information and explanations. Another side effect is that situational explanations lead to better ratings of subjective performance, while also here a significant interaction of gender and prior information is noted. Thus, contrary to expectations, a dominant role of continuous situational explanations (Explainable AI) of the intelligent vehicle for increasing trust is not confirmed and the extent of given prior information seems the deciding factor for initial trust building, which is an important aspect for the introduction of new intelligent technology into society. This is remarkable as at the same time perceived intelligence seems to be dependent on both variables. So it appears, that a vehicle being able to explain its actions may well appear more intelligent, but not necessarily appear more trustworthy.

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A Learning Assisted Approach for Electronic and Optical Characterization of Tin-Doped Titanium Oxide 2

A Learning Assisted Approach for Electronic and Optical Char...

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2nd IEEE International Conference on Artificial Intelligence in Everything, AIE 2022

作者： Vubangsi, Mercel Nforna, Edwin Akongnwi Kamsu, Brigitte Fotso Tchoffo, M. Al-Turjman, Fadi Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept. Mersin Nicosia10 Turkey Htttc Bambili University of Bamenda Nwr Computational Materials Science Lab Department of Computer Science Cameroon Htttc Bambili University of Bamenda Materials Science Research Unit Department of Fundamental Sciences Cameroon Faculty of Science University of Dschang Condensed Matter Electronics and Signal Processing Research Unit Department of Physics Cameroon University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Mersin Kyrenia10 Cyprus

ISBN: (数字)9781665474009

ISBN: (纸本)9781665474009

In this work, we evaluate the effect of variation of mole fraction of Tin in doped titanium oxide on band gap. To achieve this, we resort to Matminer's "dielectric_constant"dataset and apply an algorithm to featurize the chemical formulae of the compounds in the dataset. Using k-fold cross-validation of random forest, we obtain a Machine learning model with R2 value of 0.81 MAE=0.45. Applying our model to predict the band gap of doped Titanium oxide over a range of 6 compositions, we find that the 50% doping level has a remarkably lower band gap than all other compositions which makes it a more suitable candidate for optical applications such as in solar cells. © 2022 IEEE.

关键词： Smart contract

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Quantum Exploration-based Reinforcement Learning for Efficient Robot Path Planning in Sparse-Reward Environment

Quantum Exploration-based Reinforcement Learning for Efficie...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Chao Huang Yibei Guo Zhihui Zhu Mei Si Daniel Blankenberg Rui Liu Cognitive Robotics and AI Lab (CRAI) College of Aeronautics and Engineering Kent State University Kent OH USA Department of Computer Science Ohio State University Columbus OH USA Department of Cognitive Science Rensselaer Polytechnic Institute Troy NY USA Center for Computational Life Sciences Cleveland Clinic Cleveland OH USA

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

With the latest developments in sensors, battery, and Artificial Intelligence (AI) technologies, robots can perform missions in unstructured environments (e.g., disaster sites). However, their adaptation speed is still slow due to their limited onboard computing capability and dynamic disruption from environments, leading to inaccurate planning and delayed emergency reactions. Even though reinforcement learning increases robots’ exploration speed by fusing effective guidance, the numerous interactions make the learning time-consuming and even risky (e.g., collisions). To fundamentally improve robot adaptation speed, this work seeks help from quantum power. A novel Quantum Exploration based Dreamer model (QED) was developed to facilitate reinforcement learning explorations. QED based on stochastic quantum walker quickly explores environments, evaluates action quality, and obtains a global optimal exploration strategy; then, these high-quality exploration samples will be used to facilitate reinforcement learning speed. A theoretical benefit of QED is facilitating reinforcement learning speed without changing the underlying learning architecture, which makes the proposed QED applicable to general robot learning scenarios. To validate QED effectiveness, a robot path-planning task in an obstacle-dense environment was designed. The number of needed training episodes validated the effectiveness. The results show that QED achieves around ten times faster policy learning compared with Monte Carlo tree-based reinforcement learning methods and vanilla reinforcement learning methods.

关键词： Training Quantum mechanics Stochastic processes Reinforcement learning Robot sensing systems Robot learning Collision avoidance Artificial intelligence Quantum circuit Robots

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AirLine: Efficient Learnable Line Detection with Local Edge Voting

arXiv

引用

arXiv 2023年

作者： Lin, Xiao Wang, Chen College of Computing Georgia Institute of Technology AtlantaGA30332 United States Spatial AI & Robotics Lab The Department of Computer Science and Engineering State University of New York BuffaloNY14260 United States

Line detection is widely used in many robotic tasks such as scene recognition, 3D reconstruction, and simultaneous localization and mapping (SLAM). Compared to points, lines can provide both low-level and high-level geometrical information for downstream tasks. In this paper, we propose a novel learnable edge-based line detection algorithm, AirLine, which can be applied to various tasks. In contrast to existing learnable endpoint-based methods, which are sensitive to the geometrical condition of environments, AirLine can extract line segments directly from edges, resulting in a better generalization ability for unseen environments. To balance efficiency and accuracy, we introduce a region-grow algorithm and a local edge voting scheme for line parameterization. To the best of our knowledge, AirLine is one of the first learnable edge-based line detection methods. Our extensive experiments have shown that it retains state-of-the-art-level precision, yet with a 3 − 80× runtime acceleration compared to other learning-based methods, which is critical for low-power robots. Copyright © 2023, The Authors. All rights reserved.

关键词： robotics

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Robust Model Predictive Control with Control Barrier Functions for Autonomous Surface Vessels

Robust Model Predictive Control with Control Barrier Functio...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Wei Wang Wei Xiao Alejandro Gonzalez-Garcia Jan Swevers Carlo Ratti Daniela Rus Computer Science and Artificial Intelligence Lab (CSAIL) Massachusetts Institute of Technology Cambridge MA USA SENSEable City Laboratory Massachusetts Institute of Technology Cambridge MA USA Marine Robotics Lab Department of Mechanical Engineering College of Engineering University of Wisconsin-Madison Madison WI USA Department of Mechanical Engineering MECO Research Team KU Leuven Belgium Flanders Make@KU Leuven Belgium

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In autonomous robot navigation, the trajectories from path planners are considered to be safe regions, and deviations could endanger vessels. Model Predictive Control (MPC) stands as a popular choice for trajectory tracking problems as it naturally addresses operational constraints, such as dynamics and control constraints. Nevertheless, achieving robustness in changing environments like oceans and rivers, which are constantly subject to significant external disturbances, remains an ongoing challenge for MPC. It must consistently keep the system within a predefined safe region (such as a reference trajectory) even in the presence of model inaccuracies and perturbations. To address this challenge, we present a robust model predictive control strategy utilizing Control Barrier Functions (CBFs), which increases the disturbance-rejection abilities. We verify our method on an autonomous surface vessel in simulation and natural waters, both with external disturbances. Specifically, compared with the traditional MPC method, our proposed MPC-CBF strategy reduces tracking errors by 17.82% and 40.26% in simulations and field experiments, respectively. Although the control effort slightly increases by 7.78% and 4.20%, respectively, these results clearly demonstrate the enhanced resilience of MPC-CBF to disturbances.

关键词： Sea surface Trajectory tracking Navigation Perturbation methods Robustness Trajectory Rivers

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