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23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024

作者： Plonka, Björn S. Dreher, Christian Meixner, Andre Kartmann, Rainer Asfour, Tamim Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (纸本)9798350373578

In this paper, we present a novel approach for learning bimanual manipulation actions from human demonstration by extracting spatial constraints between affordance regions, termed affordance constraints, of the objects involved. Affordance regions are defined as object parts that provide interaction possibilities to an agent. For example, the bottom of a bottle affords the object to be placed on a surface, while its spout affords the contained liquid to be poured. We propose a novel approach to learn changes of affordance constraints in human demonstration to construct spatial bimanual action models representing object interactions. To exploit the information encoded in these spatial bimanual action models, we formulate an optimization problem to determine optimal object configurations across multiple execution keypoints while taking into account the initial scene, the learned affordance constraints, and the robot's kinematics. We evaluate the approach in simulation with two example tasks (pouring drinks and rolling dough) and compare three different definitions of affordance constraints: (i) component-wise distances between affordance regions in Cartesian space, (ii) component-wise distances between affordance regions in cylindrical space, and (iii) degrees of satisfaction of manually defined symbolic spatial affordance constraints. © 2024 IEEE.

关键词： Bottles

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KITchen: A Real-World Benchmark and Dataset for 6D Object Pose Estimation in Kitchen Environments 23

KITchen: A Real-World Benchmark and Dataset for 6D Object Po...

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23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024

作者： Younes, Abdelrahman Asfour, Tamim Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (纸本)9798350373578

Despite the recent progress on 6D object pose estimation methods for robotic grasping, a substantial performance gap persists between the capabilities of these methods on existing datasets and their efficacy in real-world grasping and mobile manipulation tasks, particularly when robots rely solely on their monocular egocentric field of view (FOV). Existing real-world datasets primarily focus on tabletop grasping scenarios, where a robot arm is placed in a fixed position and the objects are centralized within the FOV of fixed external camera(s). Assessing performance on such datasets may not accurately reflect the challenges encountered in everyday grasping and mobile manipulation tasks within kitchen environments such as retrieving objects from higher shelves, sinks, dishwashers, ovens, refrigerators, or microwaves. To address this gap, we present KITchen, a novel benchmark designed specifically for estimating the 6D poses of objects located in diverse positions within kitchen settings. For this purpose, we recorded a comprehensive dataset comprising around 205k real-world RGBD images for 111 kitchen objects captured in two distinct kitchens, utilizing a humanoid robot with its egocentric perspectives. Subsequently, we developed a semi-automated annotation pipeline, to streamline the labeling process of such datasets, resulting in the generation of 2D object labels, 2D object segmentation masks, and 6D object poses with minimal human effort. The benchmark, the dataset, and the annotation pipeline are publicly available at https://***/KITchen. © 2024 IEEE.

关键词： Anthropomorphic robots

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Comb, Prune, Distill: Towards Unified Pruning for Vision Model Compression 27

Comb, Prune, Distill: Towards Unified Pruning for Vision Mod...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Schmitt, Jonas Liu, Ruiping Zheng, Junwei Zhang, Jiaming Stiefelhagen, Rainer Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (纸本)9798331505929

Lightweight and effective models are essential for devices with limited resources, such as intelligent vehicles. Structured pruning offers a promising approach to model compression and efficiency enhancement. However, existing methods often tie pruning techniques to specific model ar-chitectures or vision tasks. To address this limitation, we propose a novel unified pruning framework Comb, Prune, Distill (CPD), which addresses both model-agnostic and task-agnostic concerns simultaneously. Our framework employs a combing step to resolve hierarchical layer-wise dependency issues, enabling architecture independence. Additionally, the pruning pipeline adaptively remove parameters based on the importance scoring metrics regardless of vision tasks. To support the model in retaining its learned information, we introduce knowledge distillation during the pruning step. Ex-tensive experiments demonstrate the generalizability of our framework, encompassing both convolutional neural network (CNN) and transformer models, as well as image classification and segmentation tasks. In image classification we achieve a speedup of up to × 4.3 with a accuracy loss of 1.8% and in semantic segmentation up to × 1.89 with a 5.1 % loss in mIoU. © 2024 IEEE.

关键词： Semantic Segmentation

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An Exploratory Study on the Relation between Grasp Types and Bimanual Categories in Manipulation Activities 23

An Exploratory Study on the Relation between Grasp Types and...

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23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024

作者： Haas, Joschka Endrikat, Mattis Krebs, Franziska Asfour, Tamim Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe76131 Germany

ISBN: (纸本)9798350373578

Understanding the intricacies in bimanual manipulation tasks is crucial for advancements in robotics and especially humanoid robotics. This exploratory study investigates the correlations between grasp type selection and bimanual coordination patterns, referred to as bimanual categories, in human manipulation tasks. To do so, we use two taxonomies: the Bimanual Manipulation Taxonomy, which defines categories in bimanual manipulation tasks and the GRASP Taxonomy, which defines human grasp types. In our analysis, we use a subset of the Yale Human Grasping Dataset, which includes natural, routine activities of housekeepers. The analysis reveals, amongst others, correlations between Tightly Coupled Symmetric bimanual coordination and Power Grasps. In addition, we identify edge cases such as handling soft and articulated objects, and self-handovers, and provide clear labeling guidelines according to the taxonomy. Soft objects were found to be predominantly handled with Lateral Pinch grasps. This study provides an initial step toward a deeper understanding of the relationship between grasp selection and bimanual coordination. © 2024 IEEE.

关键词： Taxonomies

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ModSelect: Automatic Modality Selection for Synthetic-to-Real Domain Generalization 17th

ModSelect: Automatic Modality Selection for Synthetic-to-Re...

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17th European Conference on Computer Vision, ECCV 2022

作者： Marinov, Zdravko Roitberg, Alina Schneider, David Stiefelhagen, Rainer Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (纸本)9783031250842

Modality selection is an important step when designing multimodal systems, especially in the case of cross-domain activity recognition as certain modalities are more robust to domain shift than others. However, selecting only the modalities which have a positive contribution requires a systematic approach. We tackle this problem by proposing an unsupervised modality selection method (ModSelect), which does not require any ground-truth labels. We determine the correlation between the predictions of multiple unimodal classifiers and the domain discrepancy between their embeddings. Then, we systematically compute modality selection thresholds, which select only modalities with a high correlation and low domain discrepancy. We show in our experiments that our method ModSelect chooses only modalities with positive contributions and consistently improves the performance on a SYNTHETIC→REAL domain adaptation benchmark, narrowing the domain gap. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Benchmarking

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Multi-modal Depression Estimation Based on Sub-attentional Fusion 17th

Multi-modal Depression Estimation Based on Sub-attentional ...

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17th European Conference on Computer Vision, ECCV 2022

作者： Wei, Ping-Cheng Peng, Kunyu Roitberg, Alina Yang, Kailun Zhang, Jiaming Stiefelhagen, Rainer Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (纸本)9783031250743

Failure to timely diagnose and effectively treat depression leads to over 280 million people suffering from this psychological disorder worldwide. The information cues of depression can be harvested from diverse heterogeneous resources, e.g., audio, visual, and textual data, raising demand for new effective multi-modal fusion approaches for automatic estimation. In this work, we tackle the task of automatically identifying depression from multi-modal data and introduce a sub-attention mechanism for linking heterogeneous information while leveraging Convolutional Bidirectional LSTM as our backbone. To validate this idea, we conduct extensive experiments on the public DAIC-WOZ benchmark for depression assessment featuring different evaluation modes and taking gender-specific biases into account. The proposed model yields effective results with 0.89 precision and 0.70 F1-score in detecting major depression and 4.92 MAE in estimating the severity. Our attention-based fusion module consistently outperforms conventional late fusion approaches and achieves competitive performance compared to the previously published depression estimation frameworks, while learning to diagnose the disorder end-to-end and relying on far fewer preprocessing steps. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Computer vision

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Rethinking Attention Module Design for Point Cloud Analysis 27th

Rethinking Attention Module Design for Point Cloud Analysis

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Wu, Chengzhi Wang, Kaige Zhong, Zeyun Fu, Hao Zheng, Junwei Zhang, Jiaming Pfrommer, Julius Beyerer, Jürgen Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany Fraunhofer Institute of Optronics System Technologies and Image Exploitation IOSB Karlsruhe Germany

ISBN: (纸本)9783031783944

In recent years, there have been significant advancements in applying attention mechanisms to point cloud analysis. However, attention module variants featured in various research papers often operate under diverse settings and tasks, incorporating potential training strategies. This heterogeneity poses challenges in establishing a fair comparison among these attention module variants. In this paper, we address this issue by rethinking and exploring attention module design within a consistent base framework and settings. Both global-based and local-based attention methods are studied, with a focus on the selection basis and scales of neighbors for local-based attention. Different combinations of aggregated local features and computation methods for attention scores are evaluated, ranging from the initial addition/concatenation-based approach to the widely adopted dot product-based method and the recently proposed vector attention technique. Various position encoding methods are also investigated. Our extensive experimental analysis reveals that there is no universally optimal design across diverse point cloud tasks. Instead, drawing from best practices, we propose tailored attention modules for specific tasks, leading to superior performance on point cloud classification and segmentation benchmarks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Attention mechanism Module design exploration. Point cloud data

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OAFuser: Toward Omni-Aperture Fusion for Light Field Semantic Segmentation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6225-6239页

作者： Teng, Fei Zhang, Jiaming Peng, Kunyu Wang, Yaonan Stiefelhagen, Rainer Yang, Kailun Hunan University School of Robotics National Engineering Research Center of Robot Visual Perception and Control Technology Changsha410082 China Karlsruhe Institute of Technology Institute for Anthropomatics and Robotics Karlsruhe76131 Germany Institute for Visual Computing ETH Zurich Zurich8092 Switzerland

Light field cameras are capable of capturing intricate angular and spatial details. This allows for acquiring complex light patterns and details from multiple angles, significantly enhancing the precision of image semantic segmentation. However, two significant issues arise: 1) The extensive angular information of light field cameras contains a large amount of redundant data, which is overwhelming for the limited hardware resources of intelligent agents. 2) A relative displacement difference exists in the data collected by different microlenses. To address these issues, we propose an omni-aperture fusion model (OAFuser) that leverages dense context from the central view and extracts the angular information from subaperture images to generate semantically consistent results. To simultaneously streamline the redundant information from the light field cameras and avoid feature loss during network propagation, we present a simple yet very effective subaperture fusion module (SAFM). This module efficiently embeds subaperture images in angular features, allowing the network to process each subaperture image with a minimal computational demand of only (∼1\rm GFlops). Furthermore, to address the mismatched spatial information across viewpoints, we present a center angular rectification module (CARM) to realize feature resorting and prevent feature occlusion caused by misalignment. The proposed OAFuser achieves state-of-the-art performance on four UrbanLF datasets in terms of all evaluation metrics and sets a new record of 84.93% in mIoU on the UrbanLF-Real Extended dataset, with a gain of +3.69%. The source code for OAFuser is available at https://***/FeiBryantkit/OAFuser. © 2020 IEEE.

关键词： Semantic Segmentation

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Robotic Sensorized Gastroendoscopy with Wireless Single-Hand Control

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Current Directions in Biomedical Engineering 2022年第1期8卷 66-69页

作者： García, Gabriela Fischer, Nikola Marzi, Christian Mathis-Ullrich, Franziska Health Robotics and Automation Laboratory Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe 76131 Germany

The manipulation of flexible endoscopes is a procedure that requires great dexterity since it requires the synchronization and use of both hands in parallel. Imprecise handling during gastroendoscopy could harm the digestive tract. Our solution allows the physician to use only one hand to wirelessly control the forward, backward, and tip bending motion. The proposed system provides endoscopic vision and tactile impact force sensing at the tip to detect the force applied to tissue and thus avoid damage. We experimentally evaluate the handling of the robotic system in open space and inside a medical phantom. The results revealed a training effect with less time demand for task completion and reduction of average impact force after only 5 runs. The proposed system was successfully controlled using one hand and, together with the force information, could enhance the physician's experience during endoscopy. Future work will address axial control and an intensive user study with clinical experts. © 2022 by Walter de Gruyter Berlin/Boston.

关键词： robotics

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'Thanks for the Practice!': LLM-Powered Social Robot as Tandem Language Partner at University 20

'Thanks for the Practice!': LLM-Powered Social Robot as Tand...

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20th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2025

作者： Ashok, Ashita Bruno, Barbara Helf, Tamara Berns, Karsten University of Kaiserslautern-Landau Department of Computer Science Kaiserslautern Germany Institute of Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (纸本)9798350378931

Large language models (LLMs), when integrated into social robots, have the potential to transform robot-assisted language learning by offering personalized, interactive communication. However, there is limited research exploring their potential to simultaneously reduce anxiety and enhance language-speaking skills among international university students, who often feel anxious when speaking a foreign language. This study addresses this gap by evaluating the impact of a humanoid robot powered by the OpenChat-3.5 LLM as a tandem partner for German language learning. Using a between-subjects design with 22 multilingual participants, two interaction conditions were tested: immersive (German-only) and bilingual (German-English). Our findings indicate that participants in the immersive mode reported experiencing significantly reduced perceived judgment by the robot compared to the bilingual mode. Although female participants showed a trend of greater improvement in learning gain, no significant gender differences were found. Open-ended feedback highlighted the need for enhanced contextual responses, slower speech rate, faster response times, and error corrections to enhance language speaking support. This study aims to advance social robots for learning by demonstrating the usage of generative AI in creating non-judgmental language practice scenarios. © 2025 IEEE.

关键词： Social robots

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