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DeformNet: Latent Space Modeling and Dynamics Prediction for Deformable Object Manipulation

DeformNet: Latent Space Modeling and Dynamics Prediction for...

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IEEE international Conference on Robotics and Automation (ICRA)

作者： Li, Chenchang Ai, Zihao Wu, Tong Li, Xiaosa Ding, Wenbo Xu, Huazhe Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Tsinghua Berkeley Shenzhen Inst Shenzhen Peoples R China RISC V Int Open Source Lab Shenzhen 518055 Peoples R China Tsinghua Univ Inst Interdisciplinary Informat Sci IIIS Beijing Peoples R China

ISBN: (纸本)9798350384581;9798350384574

Manipulating deformable objects is a ubiquitous task in household environments, demanding adequate representation and accurate dynamics prediction due to the objects' infinite degrees of freedom. This work proposes DeformNet, which utilizes latent space modeling with a learned 3D representation model to tackle these challenges effectively. The proposed representation model combines a PointNet encoder and a conditional neural radiance field (NeRF), facilitating a thorough acquisition of object deformations and variations in lighting conditions. To model the complex dynamics, we employ a recurrent state-space model (RSSM) that accurately predicts the transformation of the latent representation over time. Extensive simulation experiments with diverse objectives demonstrate the generalization capabilities of DeformNet for various deformable object manipulation tasks, even in the presence of previously unseen goals. Finally, we deploy DeformNet on an actual UR5 robotic arm to demonstrate its capability in real-world scenarios.

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TransformLoc: Transforming MAvs into Mobile Localization Infrastructures in Heterogeneous Swarms

TransformLoc: Transforming MAVs into Mobile Localization Inf...

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IEEE Conference on Computer Communications (IEEE INFOCOM)

作者： Wang, Haoyang Xu, Jingao Zhao, Chenyu Lu, Zihong Cheng, Yuhan Chen, Xuecheng Zhang, Xiao-Ping Liu, Yunhao Chen, Xinlei Tsinghua Univ Shenzhen Int Grad Sch Beijing Peoples R China Tsinghua Univ Sch Software Beijing Peoples R China Harbin Inst Technol Harbin Peoples R China Pengcheng Lab Shenzhen Peoples R China RISC V Int Open Source Lab Shenzhen Peoples R China

ISBN: (纸本)9798350383515;9798350383508

A heterogeneous micro aerial vehicles (MAv) swarm consists of resource-intensive but expensive advanced MAvs (AMAvs) and resource-limited but cost-effective basic MAvs (BMAvs), offering opportunities in diverse fields. Accurate and real-time localization is crucial for MAv swarms, but current practices lack a low-cost, high-precision, and real-time solution, especially for lightweight BMAvs. We find an opportunity to accomplish the task by transforming AMAvs into mobile localization infrastructures for BMAvs. However, turning this insight into a practical system is non-trivial due to challenges in location estimation with BMAvs' unknown and diverse localization errors and resource allocation of AMAvs given coupled influential factors. This study proposes TransformLoc, a new framework that transforms AMAvs into mobile localization infrastructures, specifically designed for low-cost and resource-constrained BMAvs. We first design an error-aware joint location estimation model to perform intermittent joint location estimation for BMAvs and then design a proximity-driven adaptive grouping-scheduling strategy to allocate resources of AMAvs dynamically. TransformLoc achieves a collaborative, adaptive, and cost-effective localization system suitable for large-scale heterogeneous MAv swarms. We implement TransformLoc on industrial drones and validate its performance. Results show that TransformLoc outperforms baselines including SOTA up to 68% in localization performance, motivating up to 60% navigation success rate improvement.

关键词： Micro air vehicle (MAv)

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Cooperative Multi-Type Multi-Agent Deep Reinforcement Learning for Resource Management in Space-Air-Ground integrated Networks

Cooperative Multi-Type Multi-Agent Deep Reinforcement Learni...

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ACM international Joint Conference on Pervasive and Ubiquitous Computing / ACM international Symposium on Wearable Computing (UbiComp/ISWC)

作者： Zhang, Hengxi Tang, Huaze Ding, Wenbo Zhang, Xiao-Ping Tsinghua Berkeley Shenzhen Inst Shenzhen Guangdong Peoples R China RISC V Int Open Source Lab Shenzhen Guangdong Peoples R China Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON Canada

ISBN: (纸本)9798400702006

The Space-Air-Ground integrated Network (SAGIN), integrating heterogeneous devices including low earth orbit (LEO) satellites, unmanned aerial vehicles (UAvs), and ground users (GUs), holds significant promise for the advancing smart city applications. However, resource management of the SAGIN is a challenge requiring urgent study in that inappropriate resource management will cause poor data transmission, and hence affect the services in smart cities. In this paper, we develop a comprehensive SAGIN system that encompasses five distinct communication links and propose an efficient cooperative multi-type multi-agent deep reinforcement learning (CMT-MARL) method to address the resource management issue. The experimental results highlight the efficacy of proposed CMT-MARL, as evidenced by key performance indicators such as the overall transmission rate and transmission success rate. These results underscore the potential value and feasibility of future implementation of the SAGIN.

关键词： SAGIN resource management multi-agent reinforcement learning

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MobiAir: Unleashing Sensor Mobility for City-scale and Fine-grained Air-Quality Monitoring with AirBERT 24

MobiAir: Unleashing Sensor Mobility for City-scale and Fine-...

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22nd Annual international Conference on Mobile Systems, Applications and Services (MOBISYS)

作者： Liu, Yuxuan Wang, Haoyang Man, Fanhang Xu, Jingao Dang, Fan Liu, Yunhao Zhang, Xiao-Ping Chen, Xinlei Tsinghua Univ Shenzhen Int Grad Sch Beijing Peoples R China Tsinghua Univ Sch Software Beijing Peoples R China Tsinghua Univ Global Innovat Exchange Beijing Peoples R China Pengcheng Lab Shenzhen Peoples R China RISC V Int Open Source Lab Shenzhen Peoples R China

ISBN: (纸本)9798400705816

Mobile air pollution sensing methods are developed to collect air quality data with higher spatial-temporal resolutions. However, existing methods cannot process the spatially mixed gas samples effectively due to the highly dynamic temporal and spatial fluctuations experienced by the sensor, leading to significant measurement deviations. We find an opportunity to tackle the problem by exploring the potential patterns from sensor measurements. In light of this, we propose MobiAir, a novel city-scale fine-grained air quality estimation system to deliver accurate mobile air quality data. First, we design AirBERT, a representation learning model to discern mixed gas concentrations. Second, we design a knowledge-informed training strategy leveraging massive unlabeled city-scale data to enhance the AirBERT performance. To ensure the practicality of MobiAir, we have invested significant efforts in implementing the software stack on our meticulously crafted Sensing Front-end, which has successfully gathered air quality data at a city-scale for more than 1200 hours. Experiments conducted on collected data show that MobiAir reduces sensing errors by 96.7% with only 44.9 ms latency, outperforming the SOTA baseline by 39.5%.

关键词： AQI Monitoring Mobile Crowd-Sensing Self-supervised Learning

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SolareSkin: Self-powered visible Light Sensing Through a Solar Cell E-Skin

SolareSkin: Self-powered Visible Light Sensing Through a Sol...

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ACM international Joint Conference on Pervasive and Ubiquitous Computing / ACM international Symposium on Wearable Computing (UbiComp/ISWC)

作者： Li, Jiarong Ge, Changshuo Tao, Jun Wang, Jingyang Xu, Xiaomin Chen, Xinlei Gui, Weihua Liang, Xiaojun Ding, Wenbo Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China Peng Cheng Lab RISC V Int Open Source Lab Shenzhen Peoples R China Cent South Univ Changsha Peoples R China

ISBN: (纸本)9798400702006

SolareSkin is a self-powered and ubiquitous electronic skin equipped with ultraflexible organic solar cells for visible light sensing and energy harvesting. This dual-functional system captures light signals, transforms them into electrical impulses and enables multi-class gesture and activity recognition. Its design employs a photocurrent model that allows solar cells to serve as energy harvesters and visible light sensors simultaneously. The solar cells demonstrate a decent conversion rate of incident light into electricity, supporting an efficient, sustainable operation. Additionally, the system incorporates advanced system integration with a low-powered data collection board embedded with wireless transmission modules and an intuitive user interface. An algorithm is employed for signal analysis with data pre-processing methods and several machine learning models. The data pre-processing methods comprise filtering, scaling, normalization, segmentation, and downsampling of raw sensor data to reduce noise and increase prediction accuracy. The machine learning model evaluation focuses on three algorithms: Support vector Machines (SvM), K-Nearest Neighbors (KNN), and Random Forest, due to their efficiency with high-dimensional, nonlinear data. The experimental results suggest the excellent performance of SvM in recognizing 7-class finger gestures and 7-class body activities, with accuracies of 97.3% and 96.7%, respectively. This advancement in electronic skin technology is promising for ubiquitous human-centric sensing, enabling various applications such as healthcare monitoring, human-computer interaction, and smart homes.

关键词： Electronic Skin visible light sensing Human activity recognition Energy harvesting

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Poster: Fusing Event and Depth Sensing for Dynamic Objects Localization and Tracking 24

Poster: Fusing Event and Depth Sensing for Dynamic Objects L...

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25th international Workshop on Mobile Computing Systems and Applications (HOTMOBILE)

作者： Wang, Haoyang Luo, Xinyu Ruan, Ciyu Chen, Xuecheng Ding, Wenhua Liu, Yuxuan Chen, Xinlei Tsinghua Univ Shenzhen Int Grad Sch Beijing Peoples R China South China Univ Technol Guangzhou Peoples R China Pengcheng Lab Shenzhen Peoples R China RISC V Int Open Source Lab Shenzhen Peoples R China

ISBN: (纸本)9798400704970

Background and motivation. The localization and tracking of high-speed dynamic objects find applications across various domains. For example, tracking unmanned aerial vehicles (UAvs) serves a crucial role for regulatory authorities, enabling the swift identification of illicitly operated *** work. Presently, the widespread use of cameras and Radar is observed in the realm of locating and tracking moving objects [1]. An illustrative example is [2], which integrates an RGB camera with Radar for object tracking, yielding follow-up effects in AR applications. Despite their successes, these methods encounter notable latency when tracking high-speed dynamic objects. The latency often extends into the hundreds of milliseconds due to several factors: ① cameras necessitate tens of milliseconds for exposure and may be susceptible to motion blur; ② algorithms may require a sequence of frames for optimizing localization results. This extended latency becomes a critical limitation when dealing with objects moving at speeds surpassing 10m/s, as the system is left with inadequate time for a response. Consequently, current methods fall short of meeting our requirement for precise real-time tracking of dynamic *** Insight. Nowadays, event camera, characterized by its asynchronous and motion-activated nature, boasting microsecond level temporal resolution, is finding application in high-frequency monitoring. This trend serves as a catalyst for our exploration into the integration of these asynchronous measurements with advanced vision techniques, aiming to enhance the tracking for high-speed ***. Albeit inspiring, translating it into a practical system is non-trivial and encounters significant challenges:• C1: Event burst and depth obtain delay impair object localization. Event cameras demonstrate enhanced sensitivity to changes in the environment. Crucial events triggered by objects are often overshadowed by the abundance of events tri

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Poster: Olfactory Sensing in Turbulent Airflow via Collaborative Robots 24

Poster: Olfactory Sensing in Turbulent Airflow via Collabora...

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25th international Workshop on Mobile Computing Systems and Applications (HOTMOBILE)

作者： Cheng, Yuhan Chen, Xuecheng Yang, Yixuan Wang, Haoyang Liu, Yuxuan Chen, Xinlei Tsinghua Univ Shenzhen Int Grad Sch Beijing Peoples R China Southern Univ Sci & Technol Shenzhen Peoples R China Pengcheng Lab Shenzhen Peoples R China RISC V Int Open Source Lab Shenzhen Peoples R China

ISBN: (纸本)9798400704970

Unforeseen gas leaks can swiftly create a highly flammable atmosphere, which will lead to risks of explosions, health hazards, as well as long-term environmental impacts, especially when the gas is toxic. Hence, localizing the gas source as soon as possible remains paramount to executing subsequent mitigation measures, such as shutting off valves and effectively containing or sealing the leaks. Assigning human operators to engage in these laborious activities poses a risk to their health and lives. Deploying autonomous mobile olfactory robots for gas source localization (GSL) presents a solution with great potential, as they can search the environment efficiently without risking human lives. We task gas leakage in a factory with intricate pipelines as an example as shown in fig. 1(a). Here, a fleet of robots equipped with gas concentration sensors [4] and wind sensors are dispatched to search the emission ***, existing approaches are not able to offer feasible GSL solutions for mobile olfaction-based robots in realistic environments [1]. Specifically, the patchy nature of gas plumes and the discontinuity of the concentration field confuses and traps the robots. As demonstrated by experimental data in fig. 1(b), plumes fragment into disjointed patches spatially. This intermittent characteristic introduces uncertainty in the source direction when robots devise paths using noisy and local sensory data, ultimately leading to their entrapment within these *** work aims to deploy and schedule a team of robots to conduct efficient GSL in realistic patchy gas environments. We develop GasHunter, a collaborative strategy for multiple robots by exchange-based team roles adaptation to balance the search efficiency and localization effectiveness of gas sources. Robots are assigned heterogeneous roles that represent different planning preferences. For example, some robots are responsible for inspecting spurious concentration maxima (exploiter), while othe

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A Wireless integrated System with Hybrid Embedded Sensing for the Continuous Monitoring of Bird Flight

A Wireless Integrated System with Hybrid Embedded Sensing fo...

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ACM international Joint Conference on Pervasive and Ubiquitous Computing / ACM international Symposium on Wearable Computing (UbiComp/ISWC)

作者： Mu, Shilong Chow, Ho Ngai Zhou, Mi Zhao, Runze Lei, Kai Chong Geng, Zihan Han, Yuxing Ding, Wenbo Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Shenzhen Int Grad Sch Shenzhen Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Peoples R China Tsinghua Univ Shenzhen Int Grad Sch RISC V Int Open Source Lab Shenzhen Peoples R China

ISBN: (纸本)9798400702006

The intricate flight patterns exhibited by birds have sparked considerable interest in many fields. However, traditional external visual observation has significant limitations, including susceptibility to line-of-sight occlusion, uneven lighting effects, and lack of continuous detection. In this paper, we design a hybrid embedded system that can perform bird flight postures recognition by integrating inertial measurement unit (IMU) with flexible strain sensor. The proposed system leverages a strain sensor based on laser-induced graphene (LIG), strategically positioned on the bird's wing joints, in conjunction with a high-precision IMU deployed on the bird's torso. Through a learning architecture, we achieve an impressive accuracy of 99.48% in identifying eight commonly observed bird flight postures. The integration of the proposed system offers exciting possibilities and serves as a powerful tool for advancing our understanding of the mechanisms underlying creature flight.

关键词： Flexible Strain Sensor Multimodal Deep Learning Flight Posture Recognition intelligent System.

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CircusTent: A Tool for Measuring the Performance of Atomic Memory Operations on Emerging Architectures 8th

CircusTent: A Tool for Measuring the Performance of Atomic M...

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8th Workshop on openSHMEM and Related Technologies (openSHMEM)

作者： Williams, Brody Leidel, John D. Wang, Xi Donofrio, David Chen, Yong Texas Tech Univ Lubbock TX 79409 USA Tact Comp Labs Muenster TX USA RISC V Int Open Source Lab Shenzhen Peoples R China

ISBN: (纸本)9783031048883;9783031048876

Endeavors to engineer the next generation of exascale platforms have resulted in a fundamental shift in system architectures. Orthogonal to what was once considered conventional wisdom, high performance systems designed today are characterized by heterogeneous architectures wherein distinct components are carefully combined in order to optimize system performance and energy efficiency. One unintended consequence of this new paradigm is an increasingly complex memory hierarchy that frequently spans multiple devices and may be composed of disparate memory types. Unfortunately, the effect on performance of this new memory model is not well understood. Moreover, a quantifiable, system-agnostic methodology capable of assessing the performance of the diverse memory subsystems within emerging architectures has yet to be introduced. The CircusTent benchmark suite has been introduced to fill this void by measuring system performance with respect to atomic memory operations using established parallel programming models. However, a detailed description and evaluation of CircusTent in a distributed memory environment, critical to both current and future system architectures, has yet to be produced. In this work, we rectify this shortcoming by introducing CircusTent implementations based on the openSHMEM and MPI programming models and evaluating these implementations across a variety of platforms. We then detail our conclusions and characterize our observations regarding the effect of different system interconnects, memory hierarchies, and instruction set architectures on system performance.

关键词： Benchmark Atomic memory operations openSHMEM MPI Performance

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Field Reconstruction-Based Non-Rendezvous Calibration for Low-Cost Mobile Sensors

Field Reconstruction-Based Non-Rendezvous Calibration for Lo...

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ACM international Joint Conference on Pervasive and Ubiquitous Computing / ACM international Symposium on Wearable Computing (UbiComp/ISWC)

作者： Luo, Ji Hu, Yiling Yu, Chengzhao Hong, Chaopeng Zhang, Xiao-Ping Chen, Xinlei Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Shenzhen Peoples R China Shenzhen Environm Thinking Sci & Technol ETST Co Shenzhen Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Key Lab Ecol Remediat & Carbon Sequestra Inst Environm & Ecol Shenzhen Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Peoples R China RISC V Int Open Source Lab Shenzhen Peoples R China Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON Canada Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Peoples R China RISC V Int Open Source Lab Pengcheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798400702006

Low-cost air pollution sensors (LCS) deployed on urban vehicles (e.g., taxis, buses) have emerged as a cost-effective solution for fine-grained air pollution monitoring. However, these mobile LCSs suffer from measurement drifting in real-world scenarios, necessitating a post-deployment real-time calibration. Unfortunately, the limited availability of urban real reference stations (RRS) restricts the calibration opportunities for LCSs. This paper proposes a non-rendezvous method that addresses this challenge by establishing virtual reference stations (vRS), which offer additional calibration opportunities for LCSs. Through the air pollution field reconstruction, the readings of vRSs are inferred from RRSs' data. Furthermore, a confidence assessment mechanism is developed to quantify the uncertainty of established vRSs. Finally, a field experiment is conducted to demonstrate the effectiveness of the proposed method, showcasing a 25% improvement over the advanced baseline.

关键词： Low-cost sensor post-deployment calibration field reconstruction

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