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

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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Learning to Model Diverse Driving Behaviors in Highly Interactive autonomous Driving Scenarios With Multiagent Reinforcement Learning

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IEEE Systems Journal 2025年第1期19卷 317-326页

作者： Liu, Weiwei Hu, Wenxuan Jing, Wei Lei, Lanxin Gao, Lingping Liu, Yong Zhejiang University The Advanced Perception on Robotics and Intelligent Learning Lab College of Control Science and Engineering Hangzhou310027 China Huzhou Institute of Zhejiang University Zhejiang 310027 China Alibaba DAMO Academy Autonomous Driving Lab Zhejiang 311121 China Huzhou University College of Information Engineering Zhejiang 313000 China

autonomous vehicles trained through multiagent reinforcement learning (MARL) have shown impressive results in many driving scenarios. However, the performance of these trained policies can be impacted when faced with diverse driving styles and personalities, particularly in highly interactive situations. This is because conventional MARL algorithms usually operate under the assumption of fully cooperative behavior among all agents and focus on maximizing team rewards during training. To address this issue, we introduce the personality modeling network (PeMN), which includes a cooperation value function and personality parameters to model the varied interactions in high-interactive scenarios. The PeMN also enables the training of a background traffic flow with diverse behaviors, thereby improving the performance and generalization of the ego vehicle. Our extensive experimental studies, which incorporate different personality parameters in high-interactive driving scenarios, demonstrate that the personality parameters effectively model diverse driving styles and that policies trained with PeMN demonstrate better generalization than traditional MARL methods. © 2007-2012 IEEE.

关键词： Reinforcement learning

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HortiBot: An Adaptive Multi-Arm System for Robotic Horticulture of Sweet Peppers

HortiBot: An Adaptive Multi-Arm System for Robotic Horticult...

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IEEE/RSJ International Conference on intelligent Robots and Systems (IROS)

作者： Christian Lenz Rohit Menon Michael Schreiber Melvin Paul Jacob Sven Behnke Maren Bennewitz Autonomous Intelligent Systems Lab University of Bonn Germany Center for Robotics Bonn Germany Humanoid Robots Lab University of Bonn Germany The Lamarr Institute Bonn Germany

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Horticultural tasks such as pruning and selective harvesting are labor intensive and horticultural staff are hard to find. Automating these tasks is challenging due to the semi-structured greenhouse workspaces, changing environmental conditions such as lighting, dense plant growth with many occlusions, and the need for gentle manipulation of non-rigid plant organs. In this work, we present the three-armed system HortiBot, with two arms for manipulation and a third arm as an articulated head for active perception using stereo cameras. Its perception system detects not only peppers, but also peduncles and stems in real time, and performs online data association to build a world model of pepper plants. Collision-aware online trajectory generation allows all three arms to safely track their respective targets for observation, grasping, and cutting. We integrated perception and manipulation to perform selective harvesting of peppers and evaluated the system in lab experiments. Using active perception coupled with end-effector force torque sensing for compliant manipulation, HortiBot achieves high success rates in our indoor pepper plant mock-up.

关键词： Torque Target tracking Horticulture Active perception Lighting Robot sensing systems Real-time systems Trajectory Sensors intelligent robots

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HortiBot: An Adaptive Multi-Arm System for Robotic Horticulture of Sweet Peppers

arXiv

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arXiv 2024年

作者： Lenz, Christian Menon, Rohit Schreiber, Michael Jacob, Melvin Paul Behnke, Sven Bennewitz, Maren Autonomous Intelligent Systems Lab University of Bonn Germany Humanoid Robots Lab University of Bonn Germany Center for Robotics Bonn Germany The Lamarr Institute Bonn Germany

关键词： End effectors

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autonomous Forest Inventory with Legged Robots: System Design and Field Deployment

arXiv

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arXiv 2024年

作者： Mattamala, Matias Chebrolu, Nived Casseau, Benoit Freißmuth, Leonard Frey, Jonas Tuna, Turcan Hutter, Marco Fallon, Maurice Oxford Robotics Institute The University of Oxford United Kingdom Robotic Systems Lab ETH Zurich Switzerland Technical University of Munich Germany Autonomous Learning Group Max Planck Institute for Intelligent Systems Germany

We present a solution for autonomous forest inventory with a legged robotic platform. Compared to their wheeled and aerial counterparts, legged platforms offer an attractive balance of endurance and low soil impact for forest applications. In this paper, we present the complete system architecture of our forest inventory solution which includes state estimation, navigation, mission planning, and real-time tree segmentation and trait estimation. We present preliminary results for three campaigns in forests in Finland and the UK and summarize the main outcomes, lessons, and challenges. Our UK experiment at the Forest of Dean with the ANYmal D legged platform, achieved an autonomous survey of a 0.96 ha plot in 20 min, identifying over 100 trees with typical DBH accuracy of 2 cm. © 2024, CC BY.

关键词： Machine design

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Learning to Model Diverse Driving Behaviors in Highly Interactive autonomous Driving Scenarios with Multi-Agent Reinforcement Learning

arXiv

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arXiv 2024年

作者： Weiwei, Liu Wenxuan, Hu Wei, Jing Lanxin, Lei Lingping, Gao Yong, Liu The Advanced Perception on Robotics and Intelligent Learning Lab College of Control Science and Enginneering Zhejiang University Hangzhou310027 China The Advanced Perception on Robotics and Intelligent Learning Lab Huzhou Institute Zhejiang University Huzhou China College of Information Engineering Huzhou University Huzhou China Department of Autonomous Driving Lab Alibaba DAMO Academy Hangzhou China

autonomous vehicles trained through Multi-Agent Reinforcement Learning (MARL) have shown impressive results in many driving scenarios. However, the performance of these trained policies can be impacted when faced with diverse driving styles and personalities, particularly in highly interactive situations. This is because conventional MARL algorithms usually operate under the assumption of fully cooperative behavior among all agents and focus on maximizing team rewards during training. To address this issue, we introduce the Personality Modeling Network (PeMN), which includes a cooperation value function and personality parameters to model the varied interactions in high-interactive scenarios. The PeMN also enables the training of a background traffic flow with diverse behaviors, thereby improving the performance and generalization of the ego vehicle. Our extensive experimental studies, which incorporate different personality parameters in high-interactive driving scenarios, demonstrate that the personality parameters effectively model diverse driving styles and that policies trained with PeMN demonstrate better generalization compared to traditional MARL methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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A 3-Step Optimization Framework with Hybrid Models for a Humanoid Robot's Jump Motion

arXiv

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arXiv 2025年

作者： Qi, Haoxiang Yu, Zhangguo Chen, Xuechao Liu, Yaliang Yi, Chuanku Dong, Chencheng Meng, Fei Huang, Qiang The Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing100081 China The International Joint Research Laboratory of Biomimetic Robots and Systems Ministry of Education Beijing100081 China The National Key Lab of Autonomous Intelligent Unmanned Systems Beijing100081 China

High dynamic jump motions are challenging tasks for humanoid robots to achieve environment adaptation and obstacle crossing. The trajectory optimization is a practical method to achieve high-dynamic and explosive jumping. This paper proposes a 3-step trajectory optimization framework for generating a jump motion for a humanoid robot. To improve iteration speed and achieve ideal performance, the framework comprises three sub-optimizations. The first optimization incorporates momentum, inertia, and center of pressure (CoP), treating the robot as a static reaction momentum pendulum (SRMP) model to generate corresponding trajectories. The second optimization maps these trajectories to joint space using effective Quadratic Programming (QP) solvers. Finally, the third optimization generates whole-body joint trajectories utilizing trajectories generated by previous parts. With the combined consideration of momentum and inertia, the robot achieves agile forward jump motions. A simulation and experiments (Fig. 1) of forward jump with a distance of 1.0 m and 0.5 m height are presented in this paper, validating the applicability of the proposed framework. Note to Practitioners-The motivation of this paper stems from the need to improve jumping performance of humanoid robots. By comprehensively considering factors such as robot posture, centroidal angular momentum, and landing foot placement, the algorithm enhances the robot's ability to navigate complex environments. This capability is crucial for applications that require overcoming obstacles, such as in search and rescue or inspection tasks. Improved jumping ability can significantly boost environmental adaptability, allowing robots to perform effectively in diverse conditions, and it also represents an exploration of the high-dynamic motion capabilities of humanoid robots. Future research will focus on integrating visual and perceptual information to enhance decision-making. Copyright © 2025, The Authors. All rights reserve

关键词： Anthropomorphic robots

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Automated Leaf-Level Inspection of Crops Combining UAV and UGV Robots 16th

Automated Leaf-Level Inspection of Crops Combining UAV and...

引用

16th European robotics Forum, ERF 2025

作者： Esser, Felix Marks, Elias Magistri, Federico Weyler, Jan Bultmann, Simon Zaenker, Tobias Ahmadi, Alireza Schreiber, Michael Kuhlmann, Heiner McCool, Chris Popović, Marija Stachniss, Cyrill Behnke, Sven Bennewitz, Maren Klingbeil, Lasse Institute of Geodesy and Geoinformation University of Bonn Bonn Germany Autonomous Intelligent Systems University of Bonn Bonn Germany Humanoid Robots Lab University of Bonn Bonn Germany Agricultural Robotics and Engineering University of Bonn Bonn Germany Center for Robotics University of Bonn Bonn Germany

ISBN: (纸本)9783031894701

Using areal and ground-based robotics systems is one possible component on the way to more sustainable crop production. In this work, we show, how a combination of a UAV and ground robot can be used to automate the process of single plant and even leaf-level inspection of crops in the field, as the farmer usually does manually by physically going to the plants. The work integrates UAV-based plant and weed mapping, UGV path planning and execution, viewpoint planning and control of a robot-mounted arm with a multi-camera head, as well as high-resolution 3D reconstruction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Crops

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Robust-Locomotion-by-Logic: Perturbation-Resilient Bipedal Locomotion via Signal Temporal Logic Guided Model Predictive Control

arXiv

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arXiv 2024年

作者： Gu, Zhaoyuan Zhao, Yuntian Chen, Yipu Guo, Rongming Leestma, Jennifer K. Sawicki, Gregory S. Zhao, Ye The Laboratory for Intelligent Decision and Autonomous Robots Woodruff School of Mechanical Engineering Georgia Institute of Technology AtlantaGA30313 United States The Physiology of Wearable Robotics Lab Woodruff School of Mechanical Engineering Georgia Institute of Technology AtlantaGA30313 United States

This study introduces a robust planning framework that utilizes a model predictive control (MPC) approach, enhanced by incorporating signal temporal logic (STL) specifications. This marks the first-ever study to apply STL-guided trajectory optimization for bipedal locomotion, specifically designed to handle both translational and orientational perturbations. Existing recovery strategies often struggle with reasoning complex task logic and evaluating locomotion robustness systematically, making them susceptible to failures caused by inappropriate recovery strategies or lack of robustness. To address these issues, we design an analytical stability metric for bipedal locomotion and quantify this metric using STL specifications, which guide the generation of recovery trajectories to achieve maximum robustness degree. To enable safe and computational-efficient crossed-leg maneuver, we design data-driven self-leg-collision constraints that are 1000 times faster than the traditional inverse-kinematics-based approach. Our framework outperforms a state-of-the-art locomotion controller, a standard MPC without STL, and a linear-temporal-logic-based planner in a high-fidelity dynamic simulation, especially in scenarios involving crossed-leg maneuvers. Additionally, the Cassie bipedal robot achieves robust performance under horizontal and orientational perturbations such as those observed in ship motions. These environments are validated in simulations and deployed on hardware. Furthermore, our proposed method demonstrates versatility on stepping stones and terrain-agnostic features on inclined terrains. © 2024, CC BY.

关键词： Inverse kinematics

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A Comparison of Imitation Learning Algorithms for Bimanual manipulation

arXiv

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arXiv 2024年

作者： Drolet, Michael Stepputtis, Simon Kailas, Siva Jain, Ajinkya Peters, Jan Schaal, Stefan Amor, Heni Ben The School of Computing and Augmented Intelligence Arizona State University Tempe United States The Robotics Institute Carnegie Mellon University Pittsburgh United States Intrinsic Mountain View United States The Intelligent Autonomous Systems Lab. TU Darmstadt Darmstadt Germany

Amidst the wide popularity of imitation learning algorithms in robotics, their properties regarding hyperparameter sensitivity, ease of training, data efficiency, and performance have not been well-studied in high-precision industry-inspired environments. In this work, we demonstrate the limitations and benefits of prominent imitation learning approaches and analyze their capabilities regarding these properties. We evaluate each algorithm on a complex bimanual manipulation task involving an over-constrained dynamics system in a setting involving multiple contacts between the manipulated object and the environment. While we find that imitation learning is well suited to solve such complex tasks, not all algorithms are equal in terms of handling environmental and hyperparameter perturbations, training requirements, performance, and ease of use. We investigate the empirical influence of these key characteristics by employing a carefully designed experimental procedure and learning environment. © 2024, CC BY.

关键词： Contrastive Learning

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