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IEEE-RAS International Conference on Humanoid Robots

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

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

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).

关键词： Legged locomotion Monte Carlo methods Supervised learning Humanoid robots Diffusion models Quadrupedal robots Predictive control

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Towards Unconstrained Collision Injury Protection Data Sets: Initial Surrogate Experiments for the Human Hand

Towards Unconstrained Collision Injury Protection Data Sets:...

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

作者： Robin Jeanne Kirschner Jinyu Yang Edonis Elshani Carina M. Micheler Tobias Leibbrand Dirk Müller Claudio Glowalla 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

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Safety for physical human-robot interaction (pHRI) is a major concern for all application domains. While current standardization for industrial robot applications provide safety constraints that address the onset of pain in blunt impacts, these impact thresholds are difficult to use on edged or pointed impactors. The most severe injuries occur in constrained contact scenarios, where crushing is possible. Nevertheless, situations potentially resulting in constrained contact only occur in certain areas of a workspace and design or organisational approaches can be used to avoid them. What remains are risks to the human physical integrity caused by unconstrained accidental contacts, which are difficult to avoid while maintaining robot motion efficiency. Nevertheless, the probability and severity of injuries occurring with edged or pointed impacting objects in unconstrained collisions is hardly researched. In this paper, we propose an experimental setup and procedure using two pendulums modeling human hands and arms and robots to understand the injury potential of unconstrained collisions of human hands with edged objects. Pig feet are used as ex vivo surrogate samples - as these closely resemble the physiological characteristics of human hands - to create an initial injury database on the severity of injuries caused by unconstrained edged or pointed impacts. For the effective mass range of typical lightweight robots, the data obtained show low probabilities of injuries such as skin cuts or bone/tendon injuries in unconstrained collisions when the velocity is reduced to < 0.5 m/s. Additionally, distinct differences between injury probability of the finger substitutes and the back of the hand substitutes are observed. The proposed experimental setups and procedures should be complemented by sufficient human modeling, e.g. the effective masses of human body parts, and will eventually lead to a complete understanding of the biomechanical injury potential in pHRI.

关键词： Hands Geometry Robot motion Human-robot interaction Standardization Skin Effective mass Collision avoidance Injuries Foot

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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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HAROOD: Human Activity Classification and Out-Of-Distribution Detection with Short-Range FMCW Radar

HAROOD: Human Activity Classification and Out-Of-Distributio...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Sabri Mustafa Kahya Muhammet Sami Yavuz Eckehard Steinbach Department of Computer Engineering Chair of Media Technology Munich Institute of Robotics and Machine Intelligence (MIRMI) Technical University of Munich School of Computation Information and Technology Infineon Technologies AG

We propose HAROOD as a short-range FMCW radar-based human activity classifier and out-of-distribution (OOD) detector. It aims to classify human sitting, standing, and walking activities and to detect any other moving or stationary object as OOD. We introduce a two-stage network. The first stage is trained with a novel loss function that includes intermediate reconstruction loss, intermediate contrastive loss, and triplet loss. The second stage uses the first stage’s output as its input and is trained with cross-entropy loss. It creates a simple classifier that performs the activity classification. On our dataset collected by 60 GHz short-range FMCW radar, we achieve an average classification accuracy of 96.51%. Also, we achieve an average AUROC of 95.04% as an OOD detector. Additionally, our extensive evaluations demonstrate the superiority of HAROOD over the state-of-the-art OOD detection methods in terms of standard OOD detection metrics.

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Ground-optimized SLAM with Hierarchical Loop Closure Detection in Large-scale Environment

Ground-optimized SLAM with Hierarchical Loop Closure Detecti...

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International Conference on Intelligent Transportation

作者： Huilin Yin Mina Sun Linchuan Zhang Jun Yan Johannes Betz College of Electronics and Information Engineering Tongji University Shanghai China Professorship of Autonomous Vehicle Systems TUM School of Engineering and Design Technical University Munich Garching Germany Munich Institute of Robotics and Machine Intelligence (MIRMI)

Accurate and reliable localization and mapping are crucial prerequisites for autonomous driving to achieve path planning. However, in large-scale complex dynamic environments, the traditional LiDAR loop closure detection methods that rely on radius search can easily result in false negatives, leading to the inability to correct accumulated errors effectively. To address this issue, a hierarchical LiDAR descriptor loop closure detection strategy is proposed in this paper, which detects invalid loop closures and has good viewpoint invariance. We integrate this strategy into an advanced LiDAR Inertial tightly coupled SLAM framework. In addition, to reduce the drift in the vertical direction during mapping, we introduce a ground marking algorithm and construct corresponding ground constraints in the back-end optimization. Our proposed method is evaluated on the MulRan dataset, and the experimental results show that our method could achieve lower accumulated errors than competing methods.

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PADRE - Propeller Anomaly Data REpository for UAVs various rotor fault configurations

PADRE - Propeller Anomaly Data REpository for UAVs various r...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Radosław Puchalski Marek Kołodziejczak Adam Bondyra Jinjun Rao Wojciech Giernacki Institute of Robotics and Machine Intelligence Faculty of Control Robotics and Electrical Engineering Poznan University of Technology Poznań Poland Shanghai Key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and Automation Shanghai University Shanghai China

The article presents a drone sensory database collected during flights with different types of propeller failures. Measurements from four accelerometers and four gyroscopes were collected during 20 flights with two types of faults occurring in different configurations in one, two, three or four rotors. The paper shows the architecture of the system and the procedure for acquiring and processing the data. Raw sensor outputs, pretreated data, and digitally processed signals were provided in a publicly available repository, the structure and purpose of which are discussed in the paper. The applicability and potential use of the shared data for other research are indicated. The provided repository should be helpful in developing methods for detecting and classifying faults in actuators of unmanned aerial vehicles (UAVs). It will be particularly useful for researchers working on data-driven methods. The default purpose of the dataset is to train artificial intelligence models that require large amounts of data.

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Innovations in Hardware Security: Leveraging FeFET Technology for Future Opportunities

Innovations in Hardware Security: Leveraging FeFET Technolog...

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IEEE Asia-Pacific Conference on Circuits and Systems

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

ISBN: (数字)9798350378771

ISBN: (纸本)9798350378788

As digital threats grow increasingly sophisticated, advancements in hardware security are paramount. Ferroelectric Field-Effect Transistor (FeFET) technology stands at the forefront of these innovations, leveraging unique properties to enhance security measures. By integrating ferroelectric materials with traditional FETs, FeFETs exhibit non-volatile memory behavior, making them ideal for diverse security applications. A notable application of FeFETs is in Physical Unclonable Functions (PUFs). PUFs utilize the inherent variability in Fe- FET manufacturing to create unique and unclonable identifiers, essential for secure authentication and key generation. This ensures each device is distinct, providing a robust foundation for modern security protocols. FeFETs also significantly improve random number generators (RNGs). The natural randomness in ferroelectric polarization switching generates high-quality random numbers, critical for cryptographic applications. FeFET-based RNGs offer superior entropy sources, vital for secure encryption keys and various cryptographic operations. In this talk, we will explore how FeFETs can implement compact and ultra-efficient logic-in-memory operations such as XOR, XNOR, and multiplexing (MUX). These operations are key elements in building logic-locking schemes that secure intellectual property (IP). The ability to perform these Boolean functions within the memory structure enhances security by protecting the hardware from unauthorized access and tampering. In conclusion, FeFET technology opens doors for innovative solutions to bolster hardware security, addressing critical challenges and unlocking new opportunities in secure computing.

关键词： Semiconductor device modeling Multiplexing Technological innovation Protocols Nonvolatile memory Hardware security Switches Physical unclonable function Energy efficiency FeFETs

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Unsupervised Video Anomaly Detection by Learning Consistency of Appearance and Motion

Unsupervised Video Anomaly Detection by Learning Consistency...

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2023 China Automation Congress, CAC 2023

作者： Li, Yanlong Ye, Zhenhao Wang, Yang Zhang, Chi Liu, Yuehu School of Software Engineering Xian Jiaotong University Xi'an710049 China College of Artificial Intelligence Xian Jiaotong University Xi'an710049 China National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Xian Jiaotong University Xi'an710049 China National Engineering Research Center for Visual Information and Applications Xian Jiaotong University Xi'an710049 China Institute of Artificial Intelligence and Robotics Xian Jiaotong University Xi'an710049 China

ISBN: (纸本)9798350303759

Unsupervised video anomaly detection aims to train the model on unlabeled videos containing only normal behavior and identify data instances with abnormal objects or behaviors as anomalies. Abnormal events are typically detected due to their unique appearance and motion features. Existing methods mainly rely on various appearance-motion feature fusion techniques to model the information from both modalities for anomaly detection. However, they overlook the semantic consistency between motion and appearance information, resulting in constrained model performance. To address this limitation, we directly model the relationship of semantic consistency between appearance and motion. Given the varying importance of different pixels, manually designing feature fusion methods to represent their consistency is impractical. So, we designed a two-stream feature fusion module based on cross-attention. This module constructs information fusion between appearance and motion semantics in normal videos, dynamically capturing the inherent appearance-motion semantic consistency. We further believe that, regardless of the perspective, the appearance-motion semantic consistency should be similar. Therefore, this paper introduces a new consistency loss, urging the model to fully model the semantic representation of consistency between appearance and motion in normal data, thereby identifying anomalies with lower consistency. Experimental results on various standard datasets and ablation studies have demonstrated the effectiveness of the proposed method. © 2023 IEEE.

关键词： Anomaly detection

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Style Nursing with Spatial and Semantic Guidance for Zero-Shot Traffic Scene Style Transfer 39

Style Nursing with Spatial and Semantic Guidance for Zero-Sh...

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39th Annual AAAI Conference on Artificial intelligence, AAAI 2025

作者： Wang, Zhen Lin, Zihang Yuan, Meng Liu, Yuehu Zhang, Chi School of Software Engineering Xi'an Jiaotong University China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University China National Key Laboratory of Human-Machine Hybrid Augmented Intelligence China National Engineering Research Center for Visual Information and Applications China

ISBN: (纸本)157735897X

Recent advances in text-to-image diffusion models have shown an outstanding ability in zero-shot style transfer. However, existing methods often struggle to balance preserving the semantic content of the input image and faithfully transferring the target style in line with the edit prompt. Especially when applied to complex traffic scenes with diverse objects, layouts, and stylistic variations, current diffusion models tend to exhibit Style Neglection, i.e., failing to generate the required style in the prompt. To address this issue, we propose Style Nursing, which directs the model to focus on style subject tokens in the text prompt and excites their corresponding visual activations. Moreover, we introduce Spatial and Semantic Guidance to guide the preservation of content after editing, which utilizes spatial features from the DDIM sampling process together with attention maps from the semantic reconstruction. To evaluate the performance of zero-shot style transfer methods in traffic scenes, we present STREET-6K, a new benchmark dataset comprising 6000 images showcasing diverse traffic scenes and style transfer variations, accompanied by comprehensive annotations and evaluation metrics. Our approach beats state-of-the-art image translation methods in comprehensive quantitative metrics and human evaluations on traffic scene image synthesis while seamlessly generalizing to various other types of images without training or fine-tuning. Further experiments on detection and segmentation tasks show that fine-tuning perception models on our synthesized images improves Recall and mean Intersection over Union (mIoU) by over 10% and 3% respectively in rarely-seen traffic scenes. Copyright © 2025, Association for the Advancement of Artificial intelligence (***). All rights reserved.

关键词： Semantic Segmentation

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Adaptive Spiking TD3+BC for Offline-to-Online Spiking Reinforcement Learning

Adaptive Spiking TD3+BC for Offline-to-Online Spiking Reinfo...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xiangfei Yang Jian Song Xuetao Zhang Donglin Wang School of Engineering Westlake University Hangzhou China National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center of Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Spiking reinforcement learning (SRL) has gradually received attention because of its ultra-low energy consumption, but the current SRL algorithms are almost online algorithms that are sample inefficient. It is well known that pure offline spiking reinforcement (offline RL) learning has limited performance. Therefore, we study SRL in the offline-to-online setting, which simultaneously possesses the advantages of low energy consumption and sample efficiency. To the best of our knowledge, this is the first study for offline-to-online SRL. Like offline-to-online RL, offline-to-online SRL also has the policy collapse issue. To overcome this problem, we adaptively adjust the penalty of behavior cloning term of spiking TD3+BC (SpikTD3+BC) based on the adaptability of policy to environment and propose a stable offline-to-online SRL, named AdaSpikTD3+BC. Experimental results on the D4RL benchmark tasks show that AdaSpikTD3+BC can not only avoid policy collapse but also cost about 10% energy to approach the performance of offline-to-online RL based on DNN.

关键词： Energy consumption Costs Adaptive systems Neural networks Cloning Reinforcement learning Benchmark testing

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