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检索条件"机构=Munich School of Robotics and Machine Intelligence and Robotics and System Intelligence"

共 225 条记录，以下是131-140 订阅

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Learning Barrier-Certified Polynomial Dynamical systems for Obstacle Avoidance with Robots

Learning Barrier-Certified Polynomial Dynamical Systems for ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Martin Schonger Hugo T. M. Kussaba Lingyun Chen Luis Figueredo Abdalla Swikir Aude Billard Sami Haddadin Munich Institute of Robotics and Machine Intelligence (MIRMI) Technical University of Munich (TUM) Germany School of Computer Science University of Nottingham UK Omar Al-Mukhtar University (OMU) Albaida Libya Learning Algorithms and Systems Laboratory EPFL Switzerland

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Established techniques that enable robots to learn from demonstrations are based on learning a stable dynamical system (DS). To increase the robots’ resilience to perturbations during tasks that involve static obstacle avoidance, we propose incorporating barrier certificates into an optimization problem to learn a stable and barrier-certified DS. Such optimization problem can be very complex or extremely conservative when the traditional linear parameter-varying formulation is used. Thus, different from previous approaches in the literature, we propose to use polynomial representations for DSs, which yields an optimization problem that can be tackled by sum-of-squares techniques. Finally, our approach can handle obstacle shapes that fall outside the scope of assumptions typically found in the literature concerning obstacle avoidance within the DS learning framework. Supplementary material can be found at the project webpage: https://***/abc-ds

关键词： Shape Scalability Perturbation methods Polynomials Collision avoidance Dynamical systems Task analysis

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Object-Centric Grasping Transferability: Linking Meshes to Postures

Object-Centric Grasping Transferability: Linking Meshes to P...

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IEEE-RAS International Conference on Humanoid Robots

作者： Diego Hidalgo-Carvajal Carlos Magno C. O. Valle Abdeldjallil Naceri Sami Haddadin Technical University of Munich Germany Department of Computer Engineering Chair of Robotics and Systems Intelligence (RSI) TUM School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence (MIRMI) Centre for Tactile Internet with Human-in-the-Loop (CeTI)

ISBN: (纸本)9798350309805

Attaining human hand manipulation capabilities is a sought-after goal of robotic manipulation. Several works have focused on understanding and applying human manipulation insights in robotic applications. However, few considered objects as central pieces to increase the generalization properties of existing methods. In this study, we explore context-based grasping information transferability between objects by using mesh-based object representations. To do so, we empirically labeled, in a mesh point-wise manner, 10 grasping postures onto a set of 12 purposely selected objects. Subsequently, we trained our convolutional neural network (CNN) based architecture with the mesh representation of a single object, associating grasping postures to its local regions. We tested our network across multiple objects of distinct similarity values. Results show that our network can successfully estimate non-feasible grasping regions as well as feasible grasping postures. Our results suggest the existence of an abstract relation between the predicted context-based grasping postures and the geometrical properties of both the training and test objects. Our proposed approach aims to expand grasp learning research by linking local segmented meshes to postures. Such a concept can be applied to grasp new objects using anthropomorphic robot hands.

关键词： Training Humanoid robots Grasping Convolutional neural networks

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Transcranial Magnetic Stimulation Robotic Assistant: towards a fully automated stimulation session

Transcranial Magnetic Stimulation Robotic Assistant: towards...

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IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： Ioannis Xygonakis Riccardo Seganfreddo Mazin Hamad Samuel Schneider Axel Schröder Sandro M. Krieg Bernhard Meyer Lorenzo Chiari Melissa Zavaglia Sami Haddadin Technical University of Munich TUM School of Computation Information and Technology Chair of Robotics and Systems Intelligence Munich Institute of Robotics and Machine Intelligence (MIRMI) Germany Department of Electrical Electronic and Information Engineering University of Bologna Italy Department of Neurosurgery Klinikum rechts der Isar Technical University of Munich School of Medicine Germany Department of Neurosurgery Heidelberg University Hospital Germany Centre for Tactile Internet with Human-in-the-Loop (CeTI) Technische Universität Dresden Germany

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Ahstract- Transcranial Magnetic Stimulation (TMS) is a non-invasive method for brain stimulation, commonly employed in depression treatment and pre-operative planning. With an average session duration of 30 minutes, the manual handling and positioning of the TMS coil during the treatment may become strenuous for the operator. Robotized TMS systems can help automate the overall process. Our study introduces the TMS Robotic Assistant (TMS-RA) as an end-to-end system integrated with a commercial navigated TMS. TMS-RA utilizes online calibration, eliminating additional calibration steps needed before each TMS session. Furthermore, it can be adapted for use with mobile-based nTMS systems commonly found in clinics. We have implemented an error-compensation scheme to mitigate positioning errors, effectively constraining both position and orientation errors to final mean values of 1.18 mm and $0.57^{\circ}$ . Comparing our system with a highly experienced medical expert, it is more precise and has significantly lower orientation errors, with a $3^{\circ}$ difference, while the position errors were slightly worse but remained comparable.

关键词： Coils Accuracy Transcranial magnetic stimulation Navigation Biomechatronics Depression Calibration Planning Brain stimulation Robots

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Scaling Methods of the Pelvis without Distortion for the Analysis of Bone Defects

Scaling Methods of the Pelvis without Distortion for the Ana...

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作者： Micheler, Carina M. Lang, Jan J. Wilhelm, Nikolas J. Lazic, Igor Hinterwimmer, Florian Fritz, Christian Eisenhart-Rothe, Rüdiger Von Zäh, Michael F. Burgkart, Rainer H. H. Department of Orthopaedics and Sports Orthopaedics University Hospital Rechts der Isar School of Medicine Technical University of Munich Munich Germany Institute for Machine Tools and Industrial Management School of Engineering and Design Technical University of Munich Garching near Munich Germany Chair of Non-destructive Testing School of Engineering and Design Technical University of Munich Munich Germany Munich Institute of Robotics and Machine Intelligence Department of Electrical and Computer Engineering Technical University of Munich Munich Germany Institute for Ai and Informatics in Medicine Technical University of Munich Munich Germany

For the development of new types of hip implants for acetabulum revision, it is beneficial to analyse the acetabular defects of the indication group in advance. In order to be able to specially compare the bone defects with each other, a normalisation and accompanying scaling of the pelvis is necessary. Uniform scaling is required so that the bone structures are not distorted. In the following study, three scaling methods based on the minimal bounding box and sphere principle are compared with a method using 14 landmarks on the *** landmark method is applied to determine the true scaling factor. For the comparison of the different methods, 40 female pelvic models with an acetabular defect are analysed. In the comparison of the scaling methods, the method using minimal bounding spheres shows the least deviation from the landmark method (mean difference 3.30 ± 2.17 %). Due to the fact that no preprocessing (definition of the landmarks) is required and the fast implementation of the algorithm, the minimal bounding sphere is to be preferred to the landmark method for a fast size estimation. © 2022 by Walter de Gruyter Berlin/Boston.

关键词： Spheres

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Nonlinear stiffness allows passive dynamic hopping for one-legged robots with an upright trunk

Nonlinear stiffness allows passive dynamic hopping for one-l...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Dennis Ossadnik Elisabeth Jensen Sami Haddadin Chair of Robotics Science and Systems Intelligence (RSI) Munich School of Robotics and Machine Intelligence (MSRM) Technical University of Munich Munich Germany

Template models are frequently used to simplify the control dynamics for robot hopping or running. Passive limit cycles can emerge for such systems and be exploited for energy-efficient control. A grand challenge in locomotion is trunk stabilization when the hip is offset from the center of mass (CoM). The swing phase plays a major role in this process due to the moment of inertia of the leg; however, many template models ignore the leg mass. In this work, the authors consider a robot hopper model (RHM) with a rigid trunk and leg plus a hip that is displaced from the CoM. It has been previously shown that no passive limit cycle exists for such a model given a linear hip spring. In this work, we show that passive limit cycles can be found when a nonlinear hip spring is used instead. To the authors’ knowledge, this is the first time that a passive limit cycle has been found for this type of system.

关键词： Legged locomotion Automation Conferences Limit-cycles Energy efficiency Nonlinear dynamical systems Springs

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Spatial motion planning with Pythagorean Hodograph curves

Spatial motion planning with Pythagorean Hodograph curves

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IEEE Conference on Decision and Control

作者： Jon Arrizabalaga Markus Ryll Autonomous Aerial Systems School of Engineering and Design Technical University of Munich Germany Munich Institute of Robotics and Machine Intelligence (MIRMI) Technical University of Munich

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper presents a two-stage prediction-based control scheme for embedding the environment’s geometric properties into a collision-free Pythagorean Hodograph spline, and subsequently finding the optimal path within the parameterized free space. The ingredients of this approach are twofold: First, we present a novel spatial path parameterization applicable to any arbitrary curve without prior assumptions in its adapted frame. Second, we identify the appropriateness of Pythagorean Hodograph curves for a compact and continuous definition of the path-parametric functions required by the presented spatial model. This dual-stage formulation results in a motion planning approach, where the geometric properties of the environment arise as states of the prediction model. Thus, the presented method is attractive for motion planning in dense environments. The efficacy of the approach is evaluated according to an illustrative ***: https://***/Sl6x8l7RJK8

关键词： Adaptation models Computational modeling Aerospace electronics Predictive models Cost function Planning Splines (mathematics)

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ULT-model: Towards a one-legged unified locomotion template model for forward hopping with an upright trunk

ULT-model: Towards a one-legged unified locomotion template ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Dennis Ossadnik Elisabeth Jensen Sami Haddadin Munich School of Robotics and Machine Intelligence Technical University of Munich Munich Germany

While many advancements have been made in the development of template models for describing upright-trunk locomotion, the majority of the effort has been focused on the stance phase. In this paper, we develop a new compact dynamic model as a first step toward a fully unified locomotion template model (ULT-model) of an upright-trunk forward hopping system, which will also require a unified control law in the next step. We demonstrate that all locomotion subfunctions are enabled by adding just a point foot mass and a parallel leg actuator to the well-known trunk SLIP model and that a stable limit cycle can be achieved. This brings us closer toward the ultimate goal of enabling closed-loop dynamics for anchor matching and thus achieving simple, efficient, robust and stable upright-trunk gait control, as observed in biological systems.

关键词： Legged locomotion Neuromuscular Biological system modeling Simulation Conferences Dynamics Robot control

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DropHD: Technology/Algorithm Co-Design for Reliable Energy-Efficient NVM-Based Hyper-Dimensional Computing Under Voltage Scaling

DropHD: Technology/Algorithm Co-Design for Reliable Energy-E...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Paul R. Genssler Mahta Mayahinia Simon Thomann Mehdi B. Tahoori Hussam Amrouch Semiconductor Test and Reliability University of Stuttgart Stuttgart Germany Department of Computer Science and Informatics Karlsruhe Institute of Technology Karlsruhe Germany Chair of AI Processor Design TUM School of Computation Information and Technology Technical University of Munich Munich Institute of Robotics and Machine Intelligence Munich Germany

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

Brain-inspired hyperdimensional computing (HDC) offers much more efficient computing compared to other classical deep learning and related machine learning algorithms. Unlike classical CMOS, emerging non-volatile memories (NVMs) used in the realization of HDC are susceptible to failures under voltage scaling, which is essential for energy saving. Although HDC is inherently robust against errors, this is only possible when hypervectors with a large dimension (e.g., 10,000 bits) are being used, resulting in significant energy consumption. This work demonstrates, for the first time, that different NVM technologies exhibit different error characteristics under voltage scaling. In contrast to conventional CMOS-based SRAM, we demonstrate that the error behavior is data-dependent and not captured by simple bit flips in emerging NVMs. We employ our cross-layer framework that starts from the underlying technology all the way up to the algorithm to develop the novel HDC training approach DropHD. DropHD considerably shrinks the size of hypervectors (e.g., from 10,000 bits down to merely 3000 bits), while maintaining a high inference accuracy. The use of aggressive voltage scaling reduces energy consumption by 1.6 x. DropHD further reduces it to up to 9.5 × while fully recovering the induced accuracy drop, i.e. without a tradeoff.

关键词： Training Energy consumption machine learning algorithms Nonvolatile memory Random access memory Voltage Inference algorithms

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Passer Kinematic Cues for Object Weight Prediction in a Simulated Robot-Human Handover

Passer Kinematic Cues for Object Weight Prediction in a Simu...

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IEEE-RAS International Conference on Humanoid Robots

作者： Clara Günter Luis Figueredo Joachim Hermsdörfer David W. Franklin Neuromuscular Diagnostics School of Medicine and Health Technical University of Munich Munich Germany Munich Institute of Robotics and Machine Intelligence (MIRMI) Technical University of Munich Munich Germany School of Computer Science University of Nottingham Nottingham UK Human Movement Science School of Medicine and Health Technical University of Munich Munich Germany Munich Data Science Institute (MDSI) Technical University of Munich Munich Germany

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

Object handovers, a seemingly straightforward action, involve a complex interplay of predictive and reactive control mechanisms in both partners. Understanding the cues that are used by humans to predict object properties is needed for planning natural robot handovers. In human-human interactions, the receiver can extract information from the passer’s movement. Here, we show in a VR simulated agenthuman object handover, that the human receiver can use passer kinematic cues to predict the transported object’s properties, such as weight, and preemptively adapt the grasping strategy towards them. We show that when the agent’s movement is correlated to the object weight, humans can interpret this cue and produce proportional anticipatory grip forces before object release. This adaptation is learned even when objects are presented in a random order and is strengthened with the repeated presentation of the pairing. The outcome of this study contributes to a better understanding of non-verbal cues in handover tasks and enables more transparent and efficient real-world physical robot-human interactions.

关键词： Sufficient conditions Humanoid robots Collaboration Kinematics Receivers Grasping Handover Planning Data mining

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Data Recording for Responsible robotics

Data Recording for Responsible Robotics

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IEEE Workshop on Advanced robotics and its Social Impacts, ARSO

作者： Jon Skerlj Maximilian Braun Sophia Witz Svenja Breuer Marieke Bak Sebastian Scholz Abdeldjallil Naceri Ruth Müller Sami Haddadin Iris Eisenberger Chair of Robotics and Systems Intelligence Munich Institute of Robotics and Machine Intelligence Technical University of Munich Munich Germany Department of Science Technology and Society Department of Economics and Policy School of Social Sciences and Technology School of Management Technical University of Munich Munich Germany Department of Innovation and Digitalisation in Law University of Vienna Juridicum Vienna Austria Institute of History and Ethics in Medicine School of Medicine Technical University of Munich Munich Germany Faculty of Law Institute of Public Law and Political Science University of Graz Graz Austria

The last decades have seen continuous attempts to advance the application of robotics and artificial intelligence (AI) research in several areas such as healthcare environments. However, using (semi)autonomous robots in healthcare also poses risks that need to be addressed throughout the systems' lifecycle. In this work, we present our version of a data recorder, as the output of an interdisciplinary research collaboration that addresses relevant social, ethical, legal, and technical aspects of advanced robotics in healthcare. We use social science insights from interviews and ethnographic fieldwork with relevant stakeholders as well as ethical and legal analyses to derive and implement four technical requirements for a data recorder to enhance accountability and transparency of a service humanoid robot, GARMI, assisting in healthcare. We present a tool to log and visualize data from human-robot interactions as a means of including non-expert users and other relevant stakeholders in accountability relationships, which also helps us to enhance the transparency of advanced robotics in general and to fulfill the examined requirements of the Medical Device Regulation (MDR) and the General Data Protection Regulation (GDPR).

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