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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Seliunina, Svetlana Otelepko, Artem Memmesheimer, Raphael Behnke, Sven University of Bonn Autonomous Intelligent Systems Group Computer Science Institute VI - Intelligent Systems and Robotics Lamarr Institute for Machine Learning and Artificial Intelligence Center for Robotics Germany

ISBN: (纸本)9798331531614

Robots need to perceive persons in their surroundings for safety and to interact with them. In this paper, we present a person segmentation and action classification approach that operates on 3D scans of hemisphere field of view LiDAR sensors. We recorded a data set with an Ouster OSDome-64 sensor consisting of scenes where persons perform three different actions and annotated it. We propose a method based on a MaskDINO model to detect and segment persons and to recognize their actions from combined spherical projected multi-channel representations of the LiDAR data with an additional positional encoding. Our approach demonstrates good performance for the person segmentation task and further performs well for the estimation of the person action states walking, waving, and sitting. An ablation study provides insights about the individual channel contributions for the person segmentation task. The trained models, code and dataset are made publicly available. © 2025 IEEE.

关键词： Signal encoding

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Characterizing Heterogeneous Car-Following Behaviors of Human Drivers in Mixed Traffic 4

Characterizing Heterogeneous Car-Following Behaviors of Huma...

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4th IEEE International Conference on Human-Machine systems, ICHMS 2024

作者： Sun, Meng Huang, Chunxi He, Dengbo Thrust Of Robotics And Autonomous Systems Guangzhou China Thrust Of Intelligent Transportation Guangzhou China

ISBN: (纸本)9798350315790

With an increasing number of autonomous vehicles (AVs) being tested and deployed on public roads, the sharing of the road between human-driven vehicles (HVs) and AVs will become inevitable. However, there is a lack of research on the interactions between HVs and AVs, particularly in terms of how HVs behave differently when following AVs compared to following HVs, and how this potential difference affects traffic safety and stability. To fill this gap, we utilized the Lyft dataset to investigate the variances between HV-following-AV and HV-following-HV scenarios. Hierarchical clustering was used to classify the driving styles of the following vehicles in all following events. Then, driving volatility measures and surrogate safety measures, i.e., speed volatility and reciprocal time-to-collision (reTTC) value, were used to compare the outcomes of different car-following events. The clustering results showed that human drivers exhibited four different car-following styles in the mixed traffic and when following AVs and HVs, drivers may exhibit different following styles. Compared to the leading HV, the leading AV resulted in a smaller TTC (i.e., a larger reTTC), regardless of the driving styles of the following HVs. When driving at low speeds, drivers had lower speed volatility when following AVs, indicating more stable traffic;while at high speed, when drivers were not sensitive to changes in the speed of the vehicle ahead, the leading AVs led to larger speed volatility. Future AV control algorithms should take the styles of the following HV into consideration to better optimize traffic stability and improve the safety of mixed traffic. © 2024 IEEE.

关键词： Cluster analysis

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A Comparison of Prompt Engineering Techniques for Task Planning and Execution in Service robotics 23

A Comparison of Prompt Engineering Techniques for Task Plann...

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23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024

作者： Bode, Jonas Pätzold, Bastian Memmesheimer, Raphael Behnke, Sven Computer Science Institute Vi - Intelligent Systems and Robotics Lamarr Institute for Machine Learning and Artificial Intelligence Center for Robotics University of Bonn Autonomous Intelligent Systems group Germany

ISBN: (纸本)9798350373578

Recent advances in Large Language Models (LLMs) have been instrumental in autonomous robot control and human-robot interaction by leveraging their vast general knowledge and capabilities to understand and reason across a wide range of tasks and scenarios. Previous works have investigated various prompt engineering techniques for improving the performance of LLMs to accomplish tasks, while others have proposed methods that utilize LLMs to plan and execute tasks based on the available functionalities of a given robot platform. In this work, we consider both lines of research by comparing prompt engineering techniques and combinations thereof within the application of high-level task planning and execution in service robotics. We define a diverse set of tasks and a simple set of functionalities in simulation, and measure task completion accuracy and execution time for several state-of-the-art models. We make our code, including all prompts, available at https://***/AIS-Bonn/Prompt-Engineering. © 2024 IEEE.

关键词： Human robot interaction

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All-Fabric Bi-directional Actuators for Multi-joint Assistance of Upper Limb

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Journal of Bionic Engineering 2023年第6期20卷 2661-2669页

作者： Junlin Ma Diansheng Chen Zhe Liu Jie Wei Xianglin Zhang Zihan Zeng Yongkang Jiang Institute of Robotics Beihang UniversityBeijing100191China Department of Control Science and Engineering Tongji UniversityShanghai201804China Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210China

According to clinical studies,upper limb robotic suits are vital to reduce therapist fatigue and accelerate patient *** pneumatic actuators have drawn increasing attention for the development of wearable robots due to their low weight,flexibility,and high power-to-weight ***,most of current actuators were designed for the flexion assistance of a specific joint,and that for joint extension requires further ***,designing an actuator for diverse working scenarios remains a *** this paper,we propose an all-fabric bi-directional actuator to assist the flexion and extension of the elbow,wrist,and fingers.A mathematical model is presented that predicts the deformation and guides the design of the proposed bi-directional *** further validate the applicability and adaptability of the proposed actuator for different joints,we developed a 3-DOF soft robotic *** results show that the robotic suit can assist the motion of the elbow,wrist,and finger of the subject.

关键词： Soft robotic suits Fabric-based actuators Multi-DOF motion Upper limb rehabilitation

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Trajectory Prediction Modeled by Bimodal Bivariate Distribution 8

Trajectory Prediction Modeled by Bimodal Bivariate Distribut...

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8th IEEE International Conference on Information and Communication Technology, CICT 2024

作者： Pruthi, Harshdeep Singh Singh, Ranveer Kala, Rahul Nandi, G.C. IIIT Allahabad Center of Intelligent Robotics Prayagraj India ABV-IIITM Gwalior Center for Autonomous Systems Gwalior India

ISBN: (纸本)9798331531539

Humans move through complicated, crowded situ-ations by abiding social norms, which include respecting others personal space, giving way to traffic, and avoiding collisions. Social connections occur in crowded settings in a variety of ways that are frequently inconspicuous. Deep learning methods have recently outperformed their manually crafted counterparts by understanding human interactions in a broader, data-driven manner. However, the conventional approaches often assume unimodal Gaussian distributions, limiting their ability to capture the multimodal nature of trajectory predictions. A person behind another person is equally likely to follow the person ahead or overtake him, and the future trajectory therefore has two very different future versions. We propose a deep learning-based methodology by proposing a novel approach for trajectory prediction by modeling the distribution of future possibilities as a bimodal bivariate distribution. The model architecture consists of an LSTM architecture with channel-wise attention. The architecture also contains an intent encoding that captures the long-term intent of the person. Apart from maximizing the probability of observing the demonstrated trajectory, the loss function also regularizes the curvature of the predicted trajectory. Further, to counter any skewness in the dataset, we augment the dataset during training by mirroring it on X and Y Axis around the centre of the scene. Experimental results show that the proposed approach outperforms popular approaches and several baselines. © 2024 IEEE.

关键词： Trajectories

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RoboCup@Home 2024 OPL Winner NimbRo: Anthropomorphic Service Robots Using Foundation Models for Perception and Planning

RoboCup@Home 2024 OPL Winner NimbRo: Anthropomorphic Service...

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27th RoboCup International Symposium, 2024

作者： Memmesheimer, Raphael Nogga, Jan Pätzold, Bastian Kruzhkov, Evgenii Bultmann, Simon Schreiber, Michael Bode, Jonas Karacora, Bertan Park, Juhui Savinykh, Alena Behnke, Sven Autonomous Intelligent Systems Computer Science Institute VI Lamarr Institute for Machine Learning and Artificial Intelligence and Center for Robotics University of Bonn Bonn Germany

ISBN: (纸本)9783031858581

We present the approaches and contributions of the winning team NimbRo@Home at the RoboCup@Home 2024 competition in the Open Platform League held in Eindhoven, NL. Further, we describe our hardware setup and give an overview of the results for the task stages and the final demonstration. For this year’s competition, we put a special emphasis on open-vocabulary object segmentation and grasping approaches that overcome the labeling overhead of supervised vision approaches, commonly used in RoboCup@Home. We successfully demonstrated that we can segment and grasp non-labeled objects by text descriptions. Further, we extensively employed Large Language Model (LLMs) for natural language understanding and task planning. Throughout the competition, our approaches showed robustness and generalization capabilities. A video of our performance can be found online (https://***/videos/RoboCup_2024). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Robot programming

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Robustness and Push Recovery of Humanoid Robots Using Predictive and Whole-Body Control

Robustness and Push Recovery of Humanoid Robots Using Predic...

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2024 IEEE International Conference on Unmanned systems, ICUS 2024

作者： Zhang, Shaojie Ni, Fenglei Li, Xueai Li, Xu Ji, Yi Shao, Xiangyu State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China Key Laboratory of Autonomous Intelligent Unmanned Systems Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350384185

Robustness walking and recovery ability in uneven terrains and unexpected collisions are crucial for the practical application of humanoid ***, existing methods struggle to effectively balance stability, motion safety and walking *** paper proposes a hybrid control strategy combing Nonlinear Model Predictive Control (NMPC) and Whole-Body Control (WBC) to improve the robot's walking stability and reaction safety in collisions *** NMPC generates long-term and macroscopic movements, with capture point theory being utilized for foot placements determination under external *** this foundation, the WBC regulates the robot refinedly in real-time based on task priorities and *** verify the effectiveness of the proposed method, involving uneven terrain walking and external impacts rejection. © 2024 IEEE.

关键词： Anthropomorphic robots

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Versatile Jumping of Humanoid Robots via Curriculum-Assisted Reinforcement Learning

Versatile Jumping of Humanoid Robots via Curriculum-Assisted...

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2024 China Automation Congress, CAC 2024

作者： Tan, Ruiyu Li, Xueai Ni, Fenglei Zhou, Dong Ji, Yi Shao, Xiangyu Key Laboratory of Autonomous Intelligent Unmanned Systems Harbin Institute of Technology Harbin China State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350368604

Versatile jumping is crucial for agile and adaptive behavior of humanoid robots in complex environments. However, humanoid robots have to manage impact forces while maintaining balance and stability throughout the take-off, flight, and landing phases, which imposes significant challenges for motion planning and controller design. This paper proposes a curriculum-assisted reinforcement learning framework for the humanoid robot to achieve various jumping tasks. The curriculum learning divides the jump training process into multi-stages and design different episodes in each stage to guarantee the training feasibility and generalize learned skills. Simulations on a 28 DOFs humanoid robot verify the excellent diverse jumping capability of the proposed policy. This exploration of different tasks also proves the policy’s robustness, allowing the robot quickly recover from perturbations using a variety of learned maneuvers. © 2024 IEEE.

关键词： Anthropomorphic robots

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Vibrational Feedback for a Teleoperated Continuum Robot with Non-contact Endoscope Localization

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Current Directions in Biomedical Engineering 2024年第1期10卷 17-20页

作者： Fischer, Jonas Andreas, Daniel Beckerle, Philipp Mathis-Ullrich, Franziska Marzi, Christian Friedrich-Alexander-University of Erlangen-Nuremberg Autonomous Systems and Mechatronics Paul-Gordan-Strase 3/5 Erlangen91052 Germany Friedrich-Alexander-University of Erlangen-Nuremberg Autonomous Systems and Mechatronics Erlangen91052 Germany Friedrich-Alexander-University of Erlangen-Nuremberg Surgical Planning and Robotic Cognition Lab Erlangen91052 Germany Karlsruhe Institute of Technology Institute for Anthropomatics and Robotics - Intelligent Process Automation & Robotics Karlsruhe76131 Germany

Limited or absent haptic feedback is reported as a factor hindering the continued adoption of surgical robots. This article presents a proof of concept for vibrotactile feedback integrated into a continuum robot to explore whether such feedback improves spatial perception in surgical settings. The robot is equipped with a capacitive sensor for noncontact endoscope localization, enabling spatial awareness of the robot's tool center point (TCP) within the surgical environment. The data from the sensor is processed and transmitted to a bracelet worn by the user, which generates vibrotactile feedback. The bracelet contains four vibration motors providing tactile cues for navigation and positioning of the robot's TCP. All subsystems are integrated into a unified system to deliver vibrotactile feedback to the user. When the user maneuvers the TCP of the robot near an object, they receive vibrotactile feedback via the bracelet. Thereby, the intensity of vibration increases as the TCP approaches the object, and the direction of the obstacle is mapped on the bracelet. Initial functional tests were performed and prove the functionality of the proposed system. © 2024 by Walter de Gruyter Berlin/Boston.

关键词： Capacitive sensors

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Machine Learning-based Optimal Trajectory Planning for Spacecraft Passively Flying Around a Satellite for Proximity Operations

Machine Learning-based Optimal Trajectory Planning for Space...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Barth, Andrew Roach, Dylan Ma, Ou Hernandez, Michael Ashbaugh, Ryan Fleming, Alex Department of Aerospace Engineering Intelligent Robotics and Autonomous Systems Laboratory University of Cincinnati CincinnatiOH45221 United States iMETALX Inc SausalitoCA94965 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

In-space or On-orbit Servicing, Assembly, and Manufacturing (ISAM or OSAM) activities require spacecraft to fly around a client satellite or another orbiting object in a proximity range from 100’s of meters to 0 meters in distance. When performing such proximity operations, it becomes extremely important for the servicing craft to not only avoid collision but at the same time fly close enough for better situational awareness and minimal fuel consumption. This is especially important because such orbiting spacecraft will often be low-cost CubeSats performing OSAM tasks, therefore, planning a Passive Relative Orbit (PRO) becomes essential for low-risk and energy-efficient operations. Next, the problem of how to deploy a CubeSat from a station-keeping Chief spacecraft along a transfer trajectory, followed by a PRO-based two-impulse burn maneuver with minimal fuel consumption and variable time constraints, needs to be solved. Once the task is completed, another two-burn transfer trajectory will be calculated for the servicing deputy to return to the Chief. In this paper, we use a machine learning approach to determine the ΔV burns required to achieve the relative transfer and task trajectories. Machine learning allows us to improve our mission planning capabilities and gives the system more freedom to make choices where the operator does not have clear or full insight of the complex physics under many constraints. The system was trained using the proximal policy optimization algorithm for a range of task trajectory parameters randomly selected for each training episode. The results of the trained system were validated by comparison to those produced by traditional optimization methods from our previous work. The robustness of the trained algorithm was verified by 1,000 random scenarios falling within the range of trained conditions. The study demonstrates the ability to use the trained model for a variety of situations without the need to retrain specially for each type

关键词： Orbits

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