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SlotGNN: Unsupervised Discovery of Multi-Object Representations and Visual Dynamics

SlotGNN: Unsupervised Discovery of Multi-Object Representati...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Alireza Rezazadeh Athreyi Badithela Karthik Desingh Changhyun Choi Department of Electrical and Computer Engineering University of Minnesota Minneapolis MN USA Department of Computer Science and Engineering University of Minnesota Minneapolis MN USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Learning multi-object dynamics from visual data using unsupervised techniques is challenging due to the need for robust, object representations that can be learned through robot interactions. This paper presents a novel framework with two new architectures: SlotTransport for discovering object representations from RGB images and SlotGNN for predicting their collective dynamics from RGB images and robot interactions. Our SlotTransport architecture is based on slot attention for unsupervised object discovery and uses a feature transport mechanism to maintain temporal alignment in object-centric representations. This enables the discovery of slots that consistently reflect the composition of multi-object scenes. These slots robustly bind to distinct objects, even under heavy occlusion or absence. Our SlotGNN, a novel unsupervised graph-based dynamics model, predicts the future state of multi-object scenes. SlotGNN learns a graph representation of the scene using the discovered slots from SlotTransport and performs relational and spatial reasoning to predict the future appearance of each slot conditioned on robot actions. We demonstrate the effectiveness of SlotTransport in learning object-centric features that accurately encode both visual and positional information. Further, we highlight the accuracy of SlotGNN in downstream robotic tasks, including challenging multi-object rearrangement and long-horizon prediction. Finally, our unsupervised approach proves effective in the real world. With only minimal additional data, our framework robustly predicts slots and their corresponding dynamics in real-world control tasks. Our project webpage: ***/slotgnn.

关键词： Visualization Adaptation models Accuracy Predictive models Cognition Task analysis Robots

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Detecting blindspots in colonoscopy by modelling curvature

Detecting blindspots in colonoscopy by modelling curvature

引用

IEEE international conference on robotics and automation (ICRA)

作者： Abrahams, George Herve, Anthony Bernth, Julius E. Yvon, Marc Hayee, Bu Liu, Hongbin Kings Coll London HaMMeR Lab London England IBM Europe Human Centr Innovat Ctr Zurich Switzerland Kings Coll Hosp London Gastroenterol London England

ISBN: (纸本)9781728190778

Optical colonoscopy is the gold standard for colorectal cancer screening, however even under optimal conditions only 81% of the internal tissue is inspected, in part causing up to 22% of early adenomas to be missed. Blindspots commonly occur at acute bends where the camera's view is blocked. 3D reconstruction alone is insufficient to assess screening completeness as predictions of both the seen and unseen mucosa are required. Existing works in blindspot detection nevertheless use a highly detailed 3D reconstruction as the first step, the complexity of which degrades processing speed and reliability. We demonstrate that this complexity is not needed to predict whether acute bends have been adequately inspected. We propose a parametric model of the colon with only 2 variables: radius and curvature. By incorporating curvature, our method can predict the occlusion which acute bends cause. We use CT scans from 12 patients which on average contain 20 beards. We use a custom colonoscopy simulator and, assuming known geometry, show that a curved model reliably predicts these blindspots while a non-curved model always misses them. From our frame-by-frame predictions we build a panoramic map of the tissue inspected over the procedure. We show that this correctly identifies all 10 blindspots during 3 minutes of colonoscopy. We envisage that by alerting clinicians to mucosa which they have missed in real time, they can revisit these areas, improving the detection of polyps and cancers.

关键词： Integrated optics Geometry Three-dimensional displays Colonoscopy Predictive models Real-time systems Complexity theory

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Nonlinear Dynamic Modeling and LQR Optimal Control of Wheel-Bipedal Robots

Nonlinear Dynamic Modeling and LQR Optimal Control of Wheel-...

引用

robotics, Intelligent Control and Artificial Intelligence (RICAI), international conference on

作者： Minchao Hu Jianliang Mao Xin Zhou Chuanlin Zhang College of Automation Engineering Shanghai University of Electric Power Shanghai China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

The wheel-bipedal robot integrates the advantages of both wheeled and legged systems, enabling efficient locomotion. However, many control approaches simplify this system to a wheeled linear inverted pendulum (WLIP), treating the legs and body as a unified entity and overlooking the distinct dynamics and effects of the legs. This paper derives a simplified model of the leg mechanism that captures the interactions between the robot’s leg and body dynamics. A linear quadratic regulator (LQR) controller is further designed to maintain overall balance, while a dedicated leg controller governs leg-specific movements, establishing a dynamic composite control framework. Additionally, this paper details the system architecture and development of controllers for various scenarios. Experimental results from prototype testing in real-world environments demonstrate the effectiveness and robustness of the proposed control strategies.

关键词： Legged locomotion Adaptation models Dynamics Optimal control Systems architecture Stability analysis Robustness Nonlinear dynamical systems Robots Testing

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Research on Psychological Test based on Large Language Model

Research on Psychological Test based on Large Language Model

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robotics, Artificial Intelligence and Intelligent Control (RAIIC), international conference on

作者： Zhengzheng Liu Yunfeng Kang Xinying Li Institute of Psychology Chinese Academy of Sciences Beijing China Department of Psychology University of Chinese Academy of Sciences Beijing China Dareway Software Co. Ltd Beijing China Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350352627

ISBN: (纸本)9798350352634

Traditional questionnaire-based psychological tests have many drawbacks, such as the subjects’ inaccurate understanding of the questions, incomplete answers, disguised intentions, one-way communication, closed answers, etc. This paper proposes a psychological test system design scheme based on pre-trained large language model (LLM), which makes use of LLM’s ability to learn and understand natural language and generate output. Complete the psychological test with the subjects through natural language human-computer *** the Cattell 16 Personality Factors Test (16PF) as an example, a natural corpus for large model training of psychological test was researched and compiled, and the psychological test model of two factors in 16PF was developed. Through test comparison, the psychological test model is better than the traditional questionnaire test effect.

关键词： Training Large language models Natural languages Text categorization Psychology Process control System analysis and design

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Analyzing and Improving Fault Tolerance of Learning-Based Navigation Systems 58

Analyzing and Improving Fault Tolerance of Learning-Based Na...

引用

58th ACM/IEEE Design automation conference (DAC)

作者： Wan, Zishen Anwar, Aqeel Hsiao, Yu-Shun Jia, Tianyu Reddi, Vijay Janapa Raychowdhury, Arijit Georgia Inst Technol Atlanta GA 30332 USA Harvard Univ Cambridge MA 02138 USA

ISBN: (纸本)9781665432740

Learning-based navigation systems are widely used in autonomous applications, such as robotics, unmanned vehicles and drones. Specialized hardware accelerators have been proposed for high-performance and energy-efficiency for such navigational tasks. However, transient and permanent faults are increasing in hardware systems and can catastrophically violate tasks safety. Meanwhile, traditional redundancy-based protection methods are challenging to deploy on resource-constrained edge applications. In this paper, we experimentally evaluate the resilience of navigation systems with respect to algorithms, fault models and data types from both RL training and inference. We further propose two efficient fault mitigation techniques that achieve 2x success rate and 39% quality-of-flight improvement in learning-based navigation systems.

关键词： Training Fault tolerance Navigation Fault tolerant systems Unmanned vehicles Inference algorithms Data models

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Learning physics-informed simulation models for soft robotic manipulation: A case study with dielectric elastomer actuators

Learning physics-informed simulation models for soft robotic...

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IEEE/RSJ international conference on Intelligent Robots and Systems (IROS)

作者： Lahariya, Manu Innes, Craig Develder, Chris Ramamoorthy, Subramanian Univ Ghent IMEC IDLab Technol Pk Zwijnaarde 126 B-9052 Ghent Belgium Univ Edinburgh Sch Informat 10 Crichton St Edinburgh EH8 9AB Midlothian Scotland

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Soft actuators offer a safe, adaptable approach to tasks like gentle grasping and dexterous manipulation. Creating accurate models to control such systems however is challenging due to the complex physics of deformable materials. Accurate Finite Element Method (FEM) models incur prohibitive computational complexity for closed-loop use. Using a differentiable simulator is an attractive alternative, but their applicability to soft actuators and deformable materials remains underexplored. This paper presents a framework that combines the advantages of both. We learn a differentiable model consisting of a material properties neural network and an analytical dynamics model of the remainder of the manipulation task. This physics-informed model is trained using data generated from FEM, and can be used for closed-loop control and inference. We evaluate our framework on a dielectric elastomer actuator (DEA) coin-pulling task. We simulate the task of using DEA to pull a coin along a surface with frictional contact, using FEM, and evaluate the physics-informed model for simulation, control, and inference. Our model attains <= 5% simulation error compared to FEM, and we use it as the basis for an MPC controller that requires fewer iterations to converge than model-free actor-critic, PD, and heuristic policies.

关键词： Dielectric elastomer actuators Differentiable simulator Finite element methods Model predictive control Neural Networks Physics based machine learning Soft Actor-Critic Soft robotics

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FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation

FastFlowNet: A Lightweight Network for Fast Optical Flow Est...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Kong, Lingtong Shen, Chunhua Yang, Jie Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai Peoples R China Univ Adelaide Adelaide SA Australia

ISBN: (纸本)9781728190778

Dense optical flow estimation plays a key role in many robotic vision tasks. In the past few years, with the advent of deep learning, we have witnessed great progress in optical flow estimation. However, current networks often consist of a large number of parameters and require heavy computation costs, largely hindering its application on low power-consumption devices such as mobile phones. In this paper, we tackle this challenge and design a lightweight model for fast and accurate optical flow prediction. Our proposed FastFlowNet follows the widely-used coarse-to-fine paradigm with following innovations. First, a new head enhanced pooling pyramid (HEPP) feature extractor is employed to intensify high-resolution pyramid features while reducing parameters. Second, we introduce a new center dense dilated correlation (CDDC) layer for constructing compact cost volume that can keep large search radius with reduced computation burden. Third, an efficient shuffle block decoder (SBD) is implanted into each pyramid level to accelerate flow estimation with marginal drops in accuracy. Experiments on both synthetic Sintel data and real-world KITTI datasets demonstrate the effectiveness of the proposed approach, which needs only 1/10 computation of comparable networks to achieve on par accuracy. In particular, FastFlowNet only contains 1.37M parameters;and can execute at 90 FPS (with a single GTX 1080Ti) or 5.7 FPS (embedded Jetson TX2 GPU) on a pair of Sintel images of resolution 1024 x 436.

关键词： Technological innovation Costs Head Correlation Estimation Predictive models Feature extraction

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Mobile MoCap: Retroreflector Localization On-The-Go

Mobile MoCap: Retroreflector Localization On-The-Go

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IEEE international conference on automation Science and Engineering (CASE)

作者： Gary Lvov Mark Zolotas Nathaniel Hanson Austin Allison Xavier Hubbard Michael Carvajal Taşkin Padir Institute for Experiential Robotics Northeastern University Boston MA USA

Motion capture through tracking retroreflectors obtains highly accurate pose estimation, which is frequently used in robotics. Unlike commercial motion capture systems, fiducial marker-based tracking methods, such as AprilTags, can perform relative localization without requiring a static camera setup. However, popular pose estimation methods based on fiducial markers have lower localization accuracy than commercial motion capture systems. We propose Mobile Mo-Cap, a system that utilizes inexpensive near-infrared cameras for accurate relative localization even while in motion. We present a retroreflector feature detector that performs 6- DoF (six degrees-of-freedom) tracking and operates with minimal camera exposure times to reduce motion blur. To evaluate the proposed localization technique while in motion, we mount our Mobile MoCap system, as well as an RGB camera to benchmark against fiducial markers, onto a precision-controlled linear rail and servo. The fiducial marker approach employs AprilTags, which are pervasively used for localization in robotics. We evaluate the two systems at varying distances, marker viewing angles, and relative velocities. Across all experimental conditions, our stereo-based Mobile MoCap system obtains higher position and orientation accuracy than the fiducial approach. The code for Mobile MoCap is implemented in ROS 2 and made publicly available at https//***/RIVeR-Lab/mobile_mocap

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Validation of a Novel Parallel-Actuated Shoulder Exoskeleton Robot for the Characterization of Human Shoulder Impedance

Validation of a Novel Parallel-Actuated Shoulder Exoskeleton...

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IEEE international conference on robotics and automation (ICRA)

作者： Chang, Dongjune Hunt, Justin Atkins, John Lee, Hyunglae Arizona State Univ Sch Engn Matter Transport & Energy Tempe AZ 85287 USA

ISBN: (纸本)9781728190778

This study validates the effectiveness of a recently developed parallel-actuated shoulder exoskeleton robot for the purpose of characterizing the neuromuscular properties of the human shoulder joint. In particular, shoulder mechanical impedance was quantified, which can be represented by a 2nd order system consisting of spring, damper and inertia. The shoulder exoskeleton robot, which utilizes a new type of 4-bar spherical parallel manipulator (4B-SPM), has inherently low inertia and as a result can provide fast perturbations that are often essential for characterizing neuromuscular properties. The robot was first evaluated by using a physical shoulder mockup with adjustable and known spring and mass properties. The results of the mockup test confirmed the reliability of the robot for the characterization of the mockup properties. Stiffness of the tested springs was accurately quantified with an error of less than 1.6 Nm/rad in any of the tested conditions. A pilot study with 5 human subjects further confirmed that the robot could be successfully used to quantify multi-dimensional human shoulder impedance in both pitch and yaw directions with high reliability (R-2 > 0.97). The average human shoulder stiffness and damping at around the neutral arm posture under low muscle activation (< 5% maximum voluntary contraction) were 30.9 Nm/rad and 3.0 Nms/rad, respectively.

关键词： Neuromuscular Perturbation methods Exoskeletons Shoulder Manipulators Shock absorbers Impedance

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An Omnidirectional Guidance Robot System for Visually Impaired Individuals with Nonlinear MPC

An Omnidirectional Guidance Robot System for Visually Impair...

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IEEE international conference on robotics and Biomimetics

作者： Hailei Yuan Qingquan Na Hui Zhou Li Yang School of Automation Nanjing University of Science and Technology Nanjing Jiangsu China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

In this article, we develop an intelligent guiding robot system based on omnidirectional kinesthetic feedback. This system provides a safer and more efficient solution for the independent travel of blind and visually impaired people (BVIP). The system includes a novel omnidirectional kinesthetic feedback guidance device and a platform equipped with various sensors (LiDAR, depth camera, IMU, etc.) and a computing unit. Meanwhile, we construct a perception-planning-control framework. To ensure the safety of the person during the guidance process, we develop a human-robot coupling model to describe the real-time state of the human-robot system. Tracking controllers execute these commands to drive the guiding device. Our system is deployed on the Smart Cane platform and has been validated through simulations and real-world scenarios, and demonstrate excellent trajectory tracking capability.

关键词： Couplings Laser radar Trajectory tracking Robot vision systems Predictive models Sensor systems Real-time systems Safety Root mean square Predictive control

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