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neuROSym: Deployment and Evaluation of a ROS-based Neuro-Symbolic Model for Human Motion Prediction 11

neuROSym: Deployment and Evaluation of a ROS-based Neuro-Sym...

引用

11th IEEE international conference on Cybernetics and Intelligent Systems (CIS) / 11th IEEE international conference on robotics, automation and Mechatronics (RAM)

作者： Mghames, Sariah Castri, Luca Hanheide, Marc Bellotto, Nicola Univ Lincoln Sch Comp Sci Lincoln England Univ Padua Dept Informat Engn Padua Italy

ISBN: (纸本)9798350364200;9798350364194

Autonomous mobile robots can rely on several human motion detection and prediction systems for safe and efficient navigation in human environments, but the underline model architectures can have different impacts on the trustworthiness of the robot in the real world. Among existing solutions for context-aware human motion prediction, some approaches have shown the benefit of integrating symbolic knowledge with state-of-the-art neural networks. In particular, a recent neurosymbolic architecture (NeuroSyM) has successfully embedded context with a Qualitative Trajectory Calculus (QTC) for spatial interactions representation. This work achieved better performance than neural-only baseline architectures on offline datasets. In this paper, we extend the original architecture to provide neuROSym, a ROS package for robot deployment in real-world scenarios, which can run, visualise, and evaluate previous neural-only and neuro-symbolic models for motion prediction online. We evaluated these models, NeuroSyM and a baseline SGAN, on a TIAGo robot in two scenarios with different human motion patterns. We assessed accuracy and runtime performance of the prediction models, showing a general improvement in case our neuro-symbolic architecture is used. We make the neuROSym package1 publicly available to the robotics community.

关键词： Prediction models

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RoboVQA: Multimodal Long-Horizon Reasoning for robotics

RoboVQA: Multimodal Long-Horizon Reasoning for Robotics

引用

IEEE international conference on robotics and automation (ICRA)

作者： Sermanet, Pierre Ding, Tianli Zhao, Jeffrey Xia, Fei Dwibedi, Debidatta Gopalakrishnan, Keerthana Chan, Christine Dulac-Arnold, Gabriel Maddineni, Sharath Joshi, Nikhil J. Florence, Pete Han, Wei Baruch, Robert Lu, Yao Mirchandani, Suvir Xu, Peng Sanketi, Pannag Hausman, Karol Shafran, Izhak Ichter, Brian Cao, Yuan Google DeepMind London England

ISBN: (纸本)9798350384581;9798350384574

We present a scalable, bottom-up and intrinsically diverse data collection scheme that can be used for high-level reasoning with long and medium horizons and that has 2.2x higher throughput compared to traditional narrow top-down step-by-step collection. We collect realistic data by performing any user requests within the entirety of 3 office buildings and using multiple embodiments (robot, human, human with grasping tool). With this data, we show that models trained on all embodiments perform better than ones trained on the robot data only, even when evaluated solely on robot episodes. We explore the economics of collection costs and find that for a fixed budget it is beneficial to take advantage of the cheaper human collection along with robot collection. We release a large and highly diverse (29,520 unique instructions) dataset dubbed RoboVQA containing 829,502 (video, text) pairs for robotics-focused visual question answering. We also demonstrate how evaluating real robot experiments with an intervention mechanism enables performing tasks to completion, making it deployable with human oversight even if imperfect while also providing a single performance metric. We demonstrate a single video-conditioned model named RoboVQA-VideoCoCa trained on our dataset that is capable of performing a variety of grounded high-level reasoning tasks in broad realistic settings with a cognitive intervention rate 46% lower than the zero-shot state of the art visual language model (VLM) baseline and is able to guide real robots through long-horizon tasks. The performance gap with zero-shot state-of-the-art models indicates that a lot of grounded data remains to be collected for real-world deployment, emphasizing the critical need for scalable data collection approaches. Finally, we show that video VLMs significantly outperform single-image VLMs with an average error rate reduction of 19% across all VQA tasks. Thanks to video conditioning and dataset diversity, the model can be used

关键词： Budget control

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Manipulator as a Tail: Promoting Dynamic Stability for Legged Locomotion

Manipulator as a Tail: Promoting Dynamic Stability for Legge...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Huang, Huang Loquerciol, Antonio Kumar, Ashish Thakkar, Neeraj Goldberg, Ken Malik, Jitendra Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581;9798350384574

For locomotion, is an arm on a legged robot a liability or an asset for locomotion? Biological systems evolved additional limbs beyond legs that facilitates postural control. This work shows how a manipulator can be an asset for legged locomotion at high speeds or under external perturbations, where the arm serves beyond manipulation. Since the system has 15 degrees of freedom (twelve for the legged robot and three for the arm), off-the-shelf reinforcement learning (RL) algorithms struggle to learn effective locomotion policies. Inspired by Bernstein's neurophysiological theory of animal motor learning, we develop an incremental training procedure that initially freezes some degrees of freedom and gradually releases them, using behaviour cloning (BC) from an early learning procedure to guide optimization in later learning. Simulation experiments show that our policy increases the success rate by up to 61 percentage points over the baselines. Simulation and real robot experiments suggest that our policy learns to use the arm as a "tail" to initiate robot turning at high speeds and to stabilize the quadruped under external perturbations. Quantitatively, in simulation experiments, we cut the failure rate up to 43.6% during high-speed turning and up to 31.8% for quadruped under external forces compared to using a locked arm.

关键词： Legged locomotion Training Perturbation methods Tail Reinforcement learning Arms Turning

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Edge Grasp Network: A Graph-Based SE(3)-invariant Approach to Grasp Detection

Edge Grasp Network: A Graph-Based SE(3)-invariant Approach t...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Huang, Haojie Wang, Dian Zhu, Xupeng Walters, Robin Platt, Robert Northeastern Univ Khoury Coll Comp Sci Boston MA 02115 USA

ISBN: (纸本)9798350323658

Given point cloud input, the problem of 6-DoF grasp pose detection is to identify a set of hand poses in SE(3) from which an object can be successfully grasped. This important problem has many practical applications. Here we propose a novel method and neural network model that enables better grasp success rates relative to what is available in the literature. The method takes standard point cloud data as input and works well with single-view point clouds observed from arbitrary viewing directions. Videos and code are available at https://***/edge_grasp_page/.

关键词： Neural network models

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AG-CVG: Coverage Planning with a Mobile Recharging UGV and an Energy-Constrained UAV

AG-CVG: Coverage Planning with a Mobile Recharging UGV and a...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Karapetyan, Nare Asghar, Ahmad Bilal Bhaskar, Amisha Shi, Guangyao Manocha, Dinesh Tokekar, Pratap Woods Hole Oceanog Inst WHOI Woods Hole MA 02543 USA Univ Maryland College Pk MD 20742 USA Univ Southern Calif Los Angeles CA 90007 USA

ISBN: (纸本)9798350384581;9798350384574

In this paper, we present an approach for coverage path planning for a team of an energy-constrained Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV). Both the UAV and the UGV have predefined areas that they have to cover. The goal is to perform complete coverage by both robots while minimizing the coverage time. The UGV can also serve as a mobile recharging station. The UAV and UGV need to occasionally rendezvous for recharging. We propose a heuristic method to address this NP-Hard planning problem. Our approach involves initially determining coverage paths without factoring in energy constraints. Subsequently, we cluster segments of these paths and employ graph matching to assign UAV clusters to UGV clusters for efficient recharging management. We perform numerical analysis on real-world coverage applications and show that compared with a greedy approach our method reduces rendezvous overhead on average by 11.33%. We demonstrate proof-of-concept with a team of a VOXL m500 drone and a Clearpath Jackal ground vehicle, providing a complete system from the offline algorithm to the field execution.

关键词： Heuristic methods

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Multiple Update Particle Filter: Position Estimation by Combining GNSS Pseudorange and Carrier Phase Observations

Multiple Update Particle Filter: Position Estimation by Comb...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Suzuki, Taro Chiba Inst Technol Future Robot Technol Ctr Chiba Japan

ISBN: (纸本)9798350384581;9798350384574

This paper presents an efficient method for updating particles in a particle filter (PF) to address the position estimation problem when dealing with sharp-peaked likelihood functions derived from multiple observations. Sharp-peaked likelihood functions commonly arise from millimeter-accurate distance observations of carrier phases in the global navigation satellite system (GNSS). However, when such likelihood functions are used for particle weight updates, the absence of particles within the peaks leads to all particle weights becoming zero. To overcome this problem, in this study, a straightforward and effective approach is introduced for updating particles when dealing with sharp-peaked likelihood functions obtained from multiple observations. The proposed method, termed as the multiple update PF, leverages prior knowledge regarding the spread of distribution for each likelihood function and conducts weight updates and resampling iteratively in the particle update process, prioritizing the likelihood function spreads. Experimental results demonstrate the efficacy of our proposed method, particularly when applied to position estimation utilizing GNSS pseudorange and carrier phase observations. The multiple update PF exhibits faster convergence with fewer particles when compared to the conventional PF. Moreover, vehicle position estimation experiments conducted in urban environments reveal that the proposed method outperforms conventional GNSS positioning techniques, yielding more accurate position estimates.

关键词： Iterative methods

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Evaluating Accuracy and Efficiency of Fruit Image Generation Using Generative AI Diffusion models for Agricultural robotics 39

Evaluating Accuracy and Efficiency of Fruit Image Generation...

引用

39th international conference on Image and Vision Computing New Zealand

作者： Zhao, Kun Minh Nguyen Yan, Weiqi Auckland Univ Technol Dept Comp Sci & Software Engn Auckland New Zealand

ISBN: (纸本)9798331518783;9798331518776

The automation of fruit picking using robotic systems is rapidly advancing within precision agriculture, relying heavily on the accurate detection and segmentation of fruit objects through computer vision. This study explores the use of generative artificial intelligence (AI) to create extensive fruit image datasets, concentrating on cherries, bananas, oranges, apples, and pineapples. Five generative models-Stable Diffusion v1.4, Stable Diffusion v1.5, Stable Diffusion v2, Stable Diffusion XL Refiner 1.0, and SDXL Turbo-were employed to generate 1000 images per fruit type, each set against a white background. Thresholding techniques were used to automatically extract fruit boundaries, which were then utilized to train segmentation models. The performance of these models was evaluated based on their accuracy and processing speed. SDXL Turbo consistently delivered the highest accuracy across all fruit types, though it required more processing time per image. Stable Diffusion XL Refiner 1.0 also exhibited strong accuracy but balanced performance differently. In contrast, Stable Diffusion v2 showed significant shortcomings, particularly in producing accurate images for cherries and oranges. This comparative analysis highlights the potential of advanced generative models in enhancing synthetic dataset creation for fruit object detection and segmentation in agricultural robotics. Future research will focus on refining these models to improve accuracy, broaden their applicability to a wider variety of fruits and environments, and optimize the trade-off between image quality and generation

关键词： Precision agriculture fruit detection object segmentation generative AI synthetic dataset Stable Diffusion agricultural robotics image processing computer vision automation

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Enhanced Lightweight Virtual Commissioning Technique for Human-Machine Collaboration Utilizing Meta-Digital Twin and AML Modeling 9

Enhanced Lightweight Virtual Commissioning Technique for Hum...

引用

9th international conference on automation, Control and robotics Engineering (CACRE)

作者： Ding, Kai Fan, Liuqun He, Chenxiao Long, Feng Tongji Univ Sch Mech Engn Shanghai Peoples R China Lishui Vocat & Tech Coll Intelligent Mfg Lishui Peoples R China

ISBN: (纸本)9798350350319;9798350350302

In the context of highly customized industrial applications, human-machine collaboration systems merge the efficiency and precision of collaborative robots with the adaptability and complex decision-making capabilities of humans. This integration adeptly addresses intricate manufacturing tasks that demand high sensitivity and adaptability. However, the virtual Commissioning of human-machine collaborative design schemes prior to the establishment of such systems presents challenges. These include integrating diverse heterogeneous components, the absence of a unified standard for modeling, and the need for dynamic adjustments to design schemes to enhance Commissioning efficiency across multiple engineers. Moreover, in manufacturing environments where cost and efficiency are critical, there is a demand for lightweight collaborative Commissioning tools that can swiftly resolve issues. To address these challenges, this paper leverages AML standardized modeling capabilities to develop a human-machine collaborative digital twin construction methodology. This methodology is based on meta models, meta-actions, and configurable meta processes. Furthermore, it introduces a granular progressive loading algorithm designed for a cloud-edge operational mode, which, when coupled with multi-view classification techniques, fulfills the requirements of lightweight collaboration in platforms such as web browsers/IPAD/Augmented Reality. The approach is ultimately validated through a case study focusing on battery pack wiring harness assembly units within the new energy battery sector.

关键词： Human machine collaboration Virtual commissioning Lightweight digital twin automation ML

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Video Stabilization using RAFT-based Optical Flow

Video Stabilization using RAFT-based Optical Flow

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2023 international conference on robotics and automation in Industry, ICRAI 2023

作者： Ashar, Rana Sadiq, Burhan Mohiuddin, Hira Ashraf, Saniya Imran, Muhammad Ullah, Anayat Control Automotive and Robotics Lab National Centre of Robotics and Automation Rawalpindi Pakistan Balochistan University of IT Engineering and Management Sciences Department of Electronic Engineering Quetta Pakistan

ISBN: (纸本)9781665464727

Video Stabilization is the basic need for modern-day video capture. Many methods have been proposed throughout the years including 2D and 3D-based models as well as models that use optimization and deep neural networks. This work describes the implementation of cutting-edge Recurrent All-Pairs Field Transforms (RAFT) for optical flow estimation in video stabilization. we use a pipeline that accommodates the large motion. It then passes the results to the optical flow for better accuracy. After that, it satisfies the inaccuracies of the optical flow and makes it robust to occlusion, Parallax, and moving objects. Our approach yields better results (visually and quantitatively) compared to other optimization and deep learning-based visual stabilization techniques. © 2023 IEEE.

关键词： Geometrical optics

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Autonomous Drifting with 3 Minutes of Data via Learned Tire models

Autonomous Drifting with 3 Minutes of Data via Learned Tire ...

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IEEE international conference on robotics and automation (ICRA)

作者： Djeumou, Franck Goh, Jonathan Y. M. Topcu, Ufuk Balachandran, Avinash Univ Texas Austin Austin TX 78712 USA Toyota Res Inst Los Altos CA USA

ISBN: (纸本)9798350323658

Near the limits of adhesion, the forces generated by a tire are nonlinear and intricately coupled. Efficient and accurate modelling in this region could improve safety, especially in emergency situations where high forces are required. To this end, we propose a novel family of tire force models based on neural ordinary differential equations and a neural-ExpTanh parameterization. These models are designed to satisfy physically insightful assumptions while also having sufficient fidelity to capture higher-order effects directly from vehicle state measurements. They are used as drop-in replacements for an analytical brush tire model in an existing nonlinear model predictive control framework. Experiments with a customized Toyota Supra show that scarce amounts of driving data less than three minutes is sufficient to achieve high-performance autonomous drifting on various trajectories with speeds up to 45mph. Comparisons with the benchmark model show a 4x improvement in tracking performance, smoother control inputs, and faster and more consistent computation time.

关键词： Ordinary differential equations

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