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Dynamic Electromagnetic Navigation

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arXiv 2024年

作者： Zughaibi, Jasan Nelson, Bradley J. Muehlebach, Michael Multi-Scale Robotics Lab ETH Zurich Zurich8092 Switzerland Learning and Dynamical Systems Group Max Planck Institute for Intelligent Systems Tübingen72076 Germany

Magnetic navigation offers wireless control over magnetic objects, which has important medical applications, such as targeted drug delivery and minimally invasive surgery. Magnetic navigation systems are categorized into systems using permanent magnets and systems based on electromagnets. Electromagnetic Navigation systems (eMNSs) are believed to have a superior actuation bandwidth, facilitating trajectory tracking and disturbance rejection. This greatly expands the range of potential medical applications and includes even dynamic environments as encountered in cardiovascular interventions. To showcase the dynamic capabilities of eMNSs, we successfully stabilize a (nonmagnetic) inverted pendulum on the tip of a magnetically driven arm. Our approach employs a model-based framework that leverages Lagrangian mechanics to capture the interaction between the mechanical dynamics and the magnetic field. Using system identification, we estimate unknown parameters, the actuation bandwidth, and characterize the system’s nonlinearity. To explore the limits of electromagnetic navigation and evaluate its scalability, we characterize the electrical system dynamics and perform reference measurements on a clinical-scale eMNS, affirming that the proposed dynamic control methodologies effectively translate to larger coil configurations. A state-feedback controller stabilizes the inherently unstable pendulum, and an iterative learning control scheme enables accurate tracking of non-equilibrium trajectories. Furthermore, to understand structural limitations of our control strategy, we analyze the influence of magnetic field gradients on the motion of the system. To our knowledge, this is the first demonstration to stabilize a 3D inverted pendulum through electromagnetic navigation. © 2024, CC BY-SA.

关键词： Disturbance rejection

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

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

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

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

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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

PediatricsGPT: Large Language Models as Chinese Medical Assistants for Pediatric Applications 38

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PediatricsGPT: Large Language Models as Chinese Medical Assi...

38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Yang, Dingkang Wei, Jinjie Xiao, Dongling Wang, Shunli Wu, Tong Li, Gang Li, Mingcheng Wang, Shuaibing Chen, Jiawei Jiang, Yue Xu, Qingyao Li, Ke Zhai, Peng Zhang, Lihua Academy for Engineering and Technology Fudan University Shanghai China Tencent Youtu Lab Shanghai China Cognition and Intelligent Technology Laboratory Shanghai China Engineering Research Center of AI and Robotics Ministry of Education Shanghai China AI and Unmanned Systems Engineering Research Center of Jilin Province Changchun China

Developing intelligent pediatric consultation systems offers promising prospects for improving diagnostic efficiency, especially in China, where healthcare resources are scarce. Despite recent advances in Large Language Models (LLMs) for Chinese medicine, their performance is sub-optimal in pediatric applications due to inadequate instruction data and vulnerable training procedures. To address the above issues, this paper builds PedCorpus, a high-quality dataset of over 300,000 multi-task instructions from pediatric textbooks, guidelines, and knowledge graph resources to fulfil diverse diagnostic demands. Upon well-designed PedCorpus, we propose PediatricsGPT, the first Chinese pediatric LLM assistant built on a systematic and robust training pipeline. In the continuous pre-training phase, we introduce a hybrid instruction pre-training mechanism to mitigate the internal-injected knowledge inconsistency of LLMs for medical domain adaptation. Immediately, the full-parameter Supervised Fine-Tuning (SFT) is utilized to incorporate the general medical knowledge schema into the models. After that, we devise a direct following preference optimization to enhance the generation of pediatrician-like humanistic responses. In the parameter-efficient secondary SFT phase, a mixture of universal-specific experts strategy is presented to resolve the competency conflict between medical generalist and pediatric expertise mastery. Extensive results based on the metrics, GPT-4, and doctor evaluations on distinct downstream tasks show that PediatricsGPT consistently outperforms previous Chinese medical LLMs. The project and data will be released at https://***/ydk122024/PediatricsGPT. © 2024 Neural information processing systems foundation. All rights reserved.

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PLAN-BASED RELAXED REWARD SHAPING FOR GOAL-DIRECTED TASKS 9

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PLAN-BASED RELAXED REWARD SHAPING FOR GOAL-DIRECTED TASKS

9th International Conference on Learning Representations, ICLR 2021

作者： Schubert, Ingmar Oguz, Ozgur S. Toussaint, Marc Learning and Intelligent Systems Group TU Berlin Germany Max Planck Institute for Intelligent Systems Stuttgart Germany Machine Learning and Robotics Lab University of Stuttgart Germany

In high-dimensional state spaces, the usefulness of Reinforcement Learning (RL) is limited by the problem of exploration. This issue has been addressed using potential-based reward shaping (PB-RS) previously. In the present work, we introduce Final-Volume-Preserving Reward Shaping (FV-RS). FV-RS relaxes the strict optimality guarantees of PB-RS to a guarantee of preserved long-term behavior. Being less restrictive, FV-RS allows for reward shaping functions that are even better suited for improving the sample efficiency of RL algorithms. In particular, we consider settings in which the agent has access to an approximate plan. Here, we use examples of simulated robotic manipulation tasks to demonstrate that plan-based FV-RS can indeed significantly improve the sample efficiency of RL over plan-based PB-RS. © 2021 ICLR 2021 - 9th International Conference on Learning Representations. All rights reserved.

关键词： Reinforcement learning

A Non-planar Assembly of Modular Tetrahedral-shaped Aerial Robots

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A Non-planar Assembly of Modular Tetrahedral-shaped Aerial R...

IEEE International Conference on robotics and Automation (ICRA)

作者： Obadah Wali Mohamad T. Shahab Eric Feron Robotics Intelligent Systems and Control (RISC) Lab King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

This paper presents a new design of aerial vehicles with tetrahedral geometry. We call this design the TetraQuad. The TetraQuad is a fractal modular aerial robot. A characteristic of fractals is that they have a geometric shape that can be assembled to generate the same geometry on a larger scale. Therefore multiple TetraQuad modules can be assembled to produce a larger scaled tetrahedral shaped aerial vehicle. The advantage is to have modular aerial robots that assemble in the vertical direction; this increases the rigidity of the structure, as well as reduces the wake interaction of the elevated propellers in the assembly. This work presents a design and analysis of the TetraQuad module as well as assemblies of multiple modules. A modular controller strategy is discussed. The functionality of the controller is illustrated using simulations. We validate our design with experimental flight tests.

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SLAM-Based Safe Indoor Exploration Strategy

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SLAM-Based Safe Indoor Exploration Strategy

International Conference on Autonomous Robots and Agents, ICARA

作者： Omar Mostafa Nikolaos Evangeliou Anthony Tzes Center for Artificial Intelligence & Robotics (CAIR) New York University Abu Dhabi (NYUAD) United Arab Emirates Robotics & Intelligent Systems Control Lab NYUAD United Arab Emirates CAIR NYUAD United Arab Emirates

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

This paper suggests a 2D exploration strategy for a planar space cluttered with obstacles. Rather than using point robots capable of adjusting their position and altitude instantly, this research is tailored to classical agents with circular footprints that cannot control instantly their pose. Inhere, a self-balanced dual-wheeled differential drive system is used to explore the place. The system is equipped with linear accelerometers and angular gyroscopes, a 3D-LiDAR, and a forward-facing RGB-D camera. The system performs RTAB-SLAM using the IMU and the LiDAR, while the camera is used for loop closures. The mobile agent explores the planar space using a safe skeleton approach that places the agent as far as possible from the static obstacles. During the exploration strategy, the heading is towards any offered openings of the space. This space exploration strategy has as its highest priority the agent's safety in avoiding the obstacles followed by the exploration of undetected space. Experimental studies with a ROS-enabled mobile agent are presented indicating the path planning strategy while exploring the space.

关键词： Simultaneous localization and mapping Mobile agents Cameras Path planning Skeleton Space exploration Safety Reliability Noise measurement Robots

Programmable Control of Ultrasound Swarmbots through Reinforcement Learning

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arXiv 2022年

作者： Schrage, Matthijs Medany, Mahmoud Ahmed, Daniel Acoustic Robotics Systems Lab Institute of Robotics and Intelligent Systems Department of Mechanical and Process Engineering ETH Zurich8092 Switzerland

Powered by acoustics, existing therapeutic and diagnostic procedures will become less invasive and new methods will become available that have never been available before. Acoustically driven microrobot navigation based on microbubbles is a promising approach for targeted drug delivery. Previous studies have used acoustic techniques to manipulate microbubbles in vitro and in vivo for the delivery of drugs using minimally invasive procedures. Even though many advanced capabilities and sophisticated control have been achieved for acoustically powered microrobots, there remain many challenges that remain to be solved. In order to develop the next generation of intelligent micro/nanorobots, it is highly desirable to conduct accurate identification of the micro-nanorobots and to control their dynamic motion autonomously. Here we use reinforcement learning control strategies to learn the microrobot dynamics and manipulate them through acoustic forces. The result demonstrated for the first time autonomous acoustic navigation of microbubbles in a microfluidic environment. Taking advantage of the benefit of the second radiation force, microbubbles swarm to form a large swarm, which is then driven along the desired trajectory. More than 100 thousand images were used for the training to study the unexpected dynamics of microbubbles. As a result of this work, the microrobots are validated to be controlled, illustrating a good level of robustness and providing computational intelligence to the microrobots, which enables them to navigate independently in an unstructured environment without requiring outside assistance. © 2022, CC BY.

关键词： Ultrasonics

Interactive Expressive Motion Generation Using Dynamic Movement Primitives

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arXiv 2025年

作者： Hielscher, Till Bulling, Andreas Arras, Kai O. Socially Intelligent Robotics Lab Institute for Artificial Intelligence University of Stuttgart Germany Collaborative Artificial Intelligence Group Institute for Visualization and Interactive Systems University of Stuttgart Germany

Our goal is to enable social robots to interact autonomously with humans in a realistic, engaging, and expressive manner. The 12 Principles of Animation [1] are a well-established framework animators use to create movements that make characters appear convincing, dynamic, and emotionally expressive. This paper proposes a novel approach that leverages Dynamic Movement Primitives (DMPs) to implement key animation principles, providing a learnable, explainable, modulable, online adaptable and composable model for automatic expressive motion generation. DMPs, originally developed for general imitation learning in robotics and grounded in a spring-damper system design, offer mathematical properties that make them particularly suitable for this task. Specifically, they enable modulation of the intensities of individual principles and facilitate the decomposition of complex, expressive motion sequences into learnable and parametrizable primitives. We present the mathematical formulation of the parameterized animation principles and demonstrate the effectiveness of our framework through experiments and application on three robotic platforms with different kinematic configurations, in simulation, on actual robots and in a user study. Our results show that the approach allows for creating diverse and nuanced expressions using a single base model. © 2025, CC BY-NC-ND.

关键词： Animation