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International Conference on Control, Automation and systems ( ICCAS)

作者： Tatsuo Fujikawa Kazumi Okai Yoji Yamada Nader Rajaei Tetsuya Nishimoto College of Engineering Nihon University Koriyama Japan Department of Mechanical System Engineering Nagoya University Nagoya Japan Munich Institute of Robotics and Machine Intelligence Technical University of Munich Munich Germany

An in-vivo rabbit model was developed to enable experiments for deciding the safety criteria for skin injuries in human-robot contact. The skin of the human finger was considered as a part vulnerable to harm during human-robot collaboration. The forearms and lower legs of rabbits were selected for substituting the human fingers. Computerized tomography imaging of the rabbit subjects and magnetic resonance imaging of human fingers were performed. Based on the captured images, the caudal part of the rabbit forearm and the lateral part of the rabbit lower leg were confirmed to have dimensions similar to the lateral part and the dorsal part of the human finger, respectively. Test equipment was developed to induce dynamic contacts between the rabbit parts and impactors at a velocity of 250 mm/s with a contact duration shorter than 100 ms. The dynamic pinching of fingers was represented by experiments using a wedge-shaped impactor with a rounded ridge to contact the skin and a flat-ended cylindrical impactor with a diameter of 10 mm. The skin opening and the bruising caused by the contact were investigated after the experiments. The model was confirmed to be applicable to the experiments for deciding the safety criteria.

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Unifying Complementarity Constraints and Control Barrier Functions for Safe Whole-Body Robot Control

arXiv

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arXiv 2025年

作者： Muchacho, Rafael I Cabral Laha, Riddhiman Pokorny, Florian T. Figueredo, Luis F.C. Chakraborty, Nilanjan Division of Robotics Perception and Learning KTH Royal Institute of Technology Sweden Munich Institute of Robotics and Machine Intelligence TUM Germany School of Computer Science University of Nottingham United Kingdom Department of Mechanical Engineering Stony Brook University NY United States

Safety-critical whole-body robot control demands reactive methods that ensure collision avoidance in real-time. Complementarity constraints and control barrier functions (CBF) have emerged as core tools for ensuring such safety constraints, and each represents a well-developed field. Despite addressing similar problems, their connection remains largely unexplored. This paper bridges this gap by formally proving the equivalence between these two methodologies for sampled-data, first-order systems, considering both single and multiple constraint scenarios. By demonstrating this equivalence, we provide a unified perspective on these techniques. This unification has theoretical and practical implications, facilitating the cross-application of robustness guarantees and algorithmic improvements between complementarity and CBF frameworks. We discuss these synergistic benefits and motivate future work in the comparison of the methods in more general cases. Copyright © 2025, The Authors. All rights reserved.

关键词： Optimal control systems

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Monolithic 3D Integration using BEOL FeFET: Reliability, Thermal Effects, and DNN Accuracy

Monolithic 3D Integration using BEOL FeFET: Reliability, The...

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IEEE Electron Devices Technology and Manufacturing Conference (EDTM)

作者： Shubham Kumar Yogesh Singh Chauhan Hussam Amrouch Indian Institute of Technology Kanpur India University of Stuttgart Germany TUM School of Computation Information and Technology Technical University of Munich Chair of AI Processor Design Munich Institute of Robotics and Machine Intelligence Munich Germany

In this paper, we demonstrate the impact of different chip integration techniques such as 2D, heterogeneous 3D(H3D), and monolithic 3D(M3D) on the reliability of compute-in-memory (CiM) architectures for the deep neural network (DNN). The emerging non-volatile memory ferroelectric FET (FeFET) is used for CiM. Further, we present a comprehensive thermal modeling of 2D, H3D, and M3D chips and how induced temperature degrades the accuracy of DNN. To achieve that, we have modified the open-source “DNN+NeuroSim” framework by incorporating Fe-FDSOI transistors for 2D and H3D designs and integrating back-end-of-line (BEOL) FeFETs for M3D configurations. Subsequently, we used this modified framework to assess essential performance metrics, including throughput, power density, area, etc. We showcase the degradation of 1.82%, 12.53%, and 28.43% in the inference accuracy of the DNN for 2D, H3D, and M3D configurations, respectively.

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Overcoming Blind Spots: Occlusion Considerations for Improved Autonomous Driving Safety

Overcoming Blind Spots: Occlusion Considerations for Improve...

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IEEE Symposium on Intelligent Vehicle

作者： Korbinian Moller Rainer Trauth Johannes Betz Professorship of Autonomous Vehicle Systems TUM School of Engineering and Design Technical University of Munich Garching Germany Munich Institute of Robotics and Machine Intelligence (MIRMI) Institute of Automotive Technology Technical University of Munich Garching Germany

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Our work introduces a module for assessing the trajectory safety of autonomous vehicles in dynamic environments marked by high uncertainty. We focus on occluded areas and occluded traffic participants with limited information about surrounding obstacles. To address this problem, we propose a software module that handles blind spots (BS) created by static and dynamic obstacles in urban environments. We identify potential occluded traffic participants, predict their movement, and assess the ego vehicle’s trajectory using various criticality metrics. The method offers a straightforward and modular integration into motion planning algorithms. We present critical real-world scenarios to evaluate our module and apply our approach to a publicly available trajectory planning algorithm. Our results demonstrate that safe yet efficient driving with occluded road users can be achieved by incorporating safety assessments into the planning process. The code used in this research is publicly available as open-source software and can be accessed at the following link: https://***/TUM-AVS/Frenetix-Occlusion.

关键词： Uncertainty Trajectory planning Software algorithms Urban areas Prediction algorithms Safety Trajectory

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Diffusion-based learning of contact plans for agile locomotion

arXiv

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arXiv 2024年

作者： Dhédin, Victor Ravi, Adithya Kumar Chinnakkonda Jordana, Armand Zhu, Huaijiang Meduri, Avadesh Righetti, Ludovic Schölkopf, Bernhard Khadiv, Majid Munich Institute of Robotics and Machine Intelligence Technical University of Munich Germany Tandon School of Engineering New York University United States Max-Planck Institute for Intelligent Systems Tübingen Germany

Legged robots have become capable of performing highly dynamic maneuvers in the past few years. However, agile locomotion in highly constrained environments such as stepping stones is still a challenge. In this paper, we propose a combination of model-based control, search, and learning to design efficient control policies for agile locomotion on stepping stones. In our framework, we use nonlinear model predictive control (NMPC) to generate whole-body motions for a given contact plan. To efficiently search for an optimal contact plan, we propose to use Monte Carlo tree search (MCTS). While the combination of MCTS and NMPC can quickly find a feasible plan for a given environment (a few seconds), it is not yet suitable to be used as a reactive policy. Hence, we generate a dataset for optimal goal-conditioned policy for a given scene and learn it through supervised learning. In particular, we leverage the power of diffusion models in handling multi-modality in the dataset. We test our proposed framework on a scenario where our quadruped robot Solo12 successfully jumps to different goals in a highly constrained environment (video). © 2024, CC BY.

关键词： Predictive control systems

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Optimizing Autonomous Vehicle Sensor Setups: A Framework for Coverage Analysis

Optimizing Autonomous Vehicle Sensor Setups: A Framework for...

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International Conference on Multisensor Fusion and Integration for Intelligent systems (MFI)

作者： Philipp Hafemann Simon Enrico Hahn Markus Lienkamp Department of Mobility Systems Engineering Institute of Automotive Technology and Munich Institute of Robotics and Machine Intelligence (MIRMI) Technical University of Munich Germany School of Engineering & Design Munich Germany

The performance of environment perception in autonomous vehicles is significantly influenced by the sensor setup, which is determined in the early design phase. The selection of the type and position of sensors in this phase often occurs before the availability of data processing algorithms. Therefore, the evaluation of the sensor setup can only be based on coverage metrics. This paper presents a novel framework for modeling the sensor coverage of autonomous vehicles. By discretizing the environment into grid cells, our framework analyzes each sensor and the whole setup. Our methodology systematically determines the coverage, identifies redundancy and blind spot areas, and obtains quantifiable metrics for evaluating the sensor setup efficiency. Utilizing the PyVista visualization library, we present the individual building blocks of the framework and their open-source implementation. The results from a real-world case study demonstrate our framework’s ability to identify weaknesses in sensor setup coverage. Our approach helps to develop a comprehensive understanding of the sensor coverage and, therefore, contributes to designing more effective and reliable sensing systems.

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Care3D: An Active 3D Object Detection Dataset of Real Robotic-Care Environments

Care3D: An Active 3D Object Detection Dataset of Real Roboti...

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IEEE International Symposium on Multimedia

作者： Michael G. Adam Sebastian Eger Martin Piccolrovazzi Maged Iskandar Joern Vogel Alexander Dietrich Seongjien Bien Jon Skerlj Abdeldjallil Naceri Eckehard Steinbach Alin Albu-Schaeffer Sami Haddadin Wolfram Burgard Department of Computer Engineering School of Computation Information and Technology Chair of Media Technology Technical University of Munich Munich Germany Institute of Robotics and Mechatronics German Aerospace Center (DLR) Wessling Germany Department of Computer Engineering School of Computation Information and Technology Technical University of Munich Chair of Robotics and Systems Intelligence Munich Institute of Robotics and Machine Intelligence (MIRMI) Munich Germany Engineering Department University of Technology Nuremberg Nuremberg Germany

As labor shortage increases in the health sector, the demand for assistive robotics grows. However, the needed test data to develop those robots is scarce, especially for the application of active 3D object detection, where no real data exists at all. This short paper counters this by introducing such an annotated dataset of real environments. The captured environments represent areas which are already in use in the field of robotic health care research. We further provide ground truth data within one room, for assessing SLAM algorithms running directly on a health care robot.

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Experimental Analysis of Impact Forces in Constrained Collisions According to ISO/TS 15066

Experimental Analysis of Impact Forces in Constrained Collis...

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IEEE International Conference on intelligence and Safety for robotics (ISR)

作者： Robin Jeanne Kirschner Nico Mansfeld Saeed Abdolshah Sami Haddadin Institute for Robotics and System Intelligence Munich School of Robotics and Machine Intelligence Technical University of Munich Munich Germany Technical University of Munich Munich Germany

For the user of a collaborative robot, it is important to select robot parameters and trajectories such that the task is fulfilled while ensuring human safety at the same time. In human robot interaction (HRI), constrained collisions can be particularly hazardous to the human and recently, collision test devices were developed that assess safety in such scenarios. In this paper, we propose the concept of a constrained collision force map (CCFM), which relates the robot impact velocity and the collision reaction method its parameterization to the peak collision force in a constrained collision scenario. The CCFM is a tool that will help practitioners to implement both safe and efficient HRI applications and to understand the robot's collision behavior. In this work, we derive the CCFM for three robots (UR10e, UR5e, and Franka Emika Panda) for varying contact thresholds, contact stiffnesses, and robot poses. Finally, we compare our results with the force estimation suggested by ISO/TS 15066:2016.

关键词： Conferences Force Human-robot interaction Estimation Tools Safety Trajectory

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Towards Safe Robot Use with Edged or Pointed Objects: A Surrogate Study Assembling a Human Hand Injury Protection Database

Towards Safe Robot Use with Edged or Pointed Objects: A Surr...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Robin Jeanne Kirschner Carina M. Micheler Yangcan Zhou Sebastian Siegner Mazin Hamad Claudio Glowalla Jan Neumann Nader Rajaei Rainer Burgkart Sami Haddadin Chair for Robotics and Systems Intelligence Munich Institute of Robotics and Machine Intelligence Technical University of Munich Munich Germany Department of Orthopaedics and Sports Orthopaedics Klinikum Rechts der Isar TUM School of Medicine Technical University of Munich Munich Germany Institute for Machine Tools and Industrial Management TUM School of Engineering and Design Technical University of Munich Garching Near Munich Germany Endoprothetikzentrum der Maximalversorgung BG Hospital Murnau Murnau Germany Department of Diagnostic and Interventional Radiology Klinikum Rechts der Isar TUM School of Medicine Technical University of Munich Munich Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The use of pointed or edged tools or objects is one of the most challenging aspects of today’s application of physical human-robot interaction (pHRI). One reason for this is that the severity of harm caused by such edged or pointed impactors is less well studied than for blunt impactors. Consequently, the standards specify well-reasoned force and pressure thresholds for blunt impactors and advise avoiding any edges and corners in contacts. Nevertheless, pointed or edged impactor geometries cannot be completely ruled out in real pHRI applications. For example, to allow edged or pointed tools such as screwdrivers near human operators, the knowledge of injury severity needs to be extended so that robot integrators can perform well-reasoned, time-efficient risk assessments. In this paper, we provide the initial datasets on injury prevention for the human hand based on drop tests with surrogates for the human hand, namely pig claws and chicken drumsticks. We then demonstrate the ease and efficiency of robot use using the dataset for contact on two examples. Finally, our experiments provide a set of injuries that may also be expected for human subjects under certain robot mass-velocity constellations in collisions. To extend this work, testing on human samples and a collaborative effort from research institutes worldwide is needed to create a comprehensive human injury avoidance database for any pHRI scenario and thus for safe pHRI applications including edged and pointed geometries.

关键词： Geometry Databases Collaboration Skin Collision avoidance Protection Injuries

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SRI-Graph: A Novel Scene-Robot Interaction Graph for Robust Scene Understanding

SRI-Graph: A Novel Scene-Robot Interaction Graph for Robust ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Dong Yang Xiao Xu Mengchen Xiong Edwin Babaians Eckehard Steinbach Department of Computer Engineering School of Computation Information and Technology Chair of Media Technology (LMT) and Munich Institute of Robotics and Machine Intelligence (MIRMI) Technical University of Munich (TUM) Germany

We propose a novel scene-robot interaction graph (SRI-Graph) that exploits the known position of a mobile manipulator for robust and accurate scene understanding. Compared to the state-of-the-art scene graph approaches, the proposed SRI-Graph captures not only the relationships between the objects, but also the relationships between the robot manipulator and objects with which it interacts. To improve the detection accuracy of spatial relationships, we leverage the 3D position of the mobile manipulator in addition to RGB images. The manipulator's ego information is crucial for a successful scene understanding when the relationships are visually uncertain. The proposed model is validated for a real-world 3D robot-assisted feeding task. We release a new dataset named 3DRF-Pos for training and validation. We also develop a tool, named LabelImg-Rel, as an extension of the open-sourced image annotation tool LabelImg for a convenient annotation in robot-environment interaction scenarios * . Our experimental results using the Movo platform show that SRI-Graph outperforms the state-of-the-art approach and improves detection accuracy by up to 9.83%.

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