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CageCoOpt: Enhancing Manipulation Robustness through Caging-Guided Morphology and Policy Co-Optimization

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arXiv 2024年

作者： Dong, Yifei Han, Shaohang Cheng, Xianyi Friedl, Werner Cabral Muchacho, Rafael I. Roa, Máximo A. Tumova, Jana Pokorny, Florian T. The division of Robotics Perception and Learning KTH Royal Institute of Technology Stockholm10044 Sweden Department of Mechanical Engineering and Material Science Duke University DurhamNC27708 United States Wessling82234 Germany

Uncertainties in contact dynamics and object geometry remain significant barriers to robust robotic manipulation. Caging mitigates these uncertainties by constraining an object’s mobility without requiring precise contact modeling. However, existing caging research has largely treated morphology and policy optimization as separate problems, overlooking their inherent synergy. In this paper, we introduce CageCoOpt, a hierarchical framework that jointly optimizes manipulator morphology and control policy for robust manipulation. The framework employs reinforcement learning for policy optimization at the lower level and multitask Bayesian optimization for morphology optimization at the upper level. A robustness metric in caging, Minimum Escape Energy, is incorporated into the objectives of both levels to promote caging configurations and enhance manipulation robustness. The evaluation results through four manipulation tasks demonstrate that co-optimizing morphology and policy improves success rates under uncertainties, establishing caging-guided co-optimization as a viable approach for robust manipulation. © 2024, CC BY.

关键词： Macroinvertebrates

Robust STL Control Synthesis under Maximal Disturbance Sets

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Robust STL Control Synthesis under Maximal Disturbance Sets

IEEE Conference on Decision and Control

作者： Joris Verhagen Lars Lindemann Jana Tumova Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Thomas Lord Department of Computer Science University of Southern California Los Angeles CA USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This work addresses maximally robust control synthesis under unknown disturbances. We consider a nonlinear system, subject to a Signal Temporal Logic (STL) specification and jointly synthesize the maximal possible disturbance bounds and the corresponding controllers that ensure the STL specification is satisfied under these bounds. Many works have considered STL satisfaction under given bounded disturbances yet, to the authors’ best knowledge, this is the first work that aims to maximize the permissible disturbance set and find corresponding maximally robust controllers. We, therefore, introduce disturbance robustness as a model-based robustness metric for STL planning and control synthesis. We extend the notion of disturbance-robust semantics for STL, which is a property of a specification, dynamical system, and controller, and provide an algorithm for maximally robust controllers satisfying an STL specification using Hamilton-Jacobi reachability. We show its soundness and provide a simulation example with an Autonomous Underwater Vehicle (AUV).

关键词： Measurement Robust control Heuristic algorithms Semantics Control systems Robustness Planning Logic Dynamical systems Nonlinear systems

Robust STL Control Synthesis under Maximal Disturbance Sets

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arXiv 2024年

作者： Verhagen, Joris Lindemann, Lars Tumova, Jana The Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden The Thomas Lord Department of Computer Science University of Southern California Los AngelesCA United States

This work addresses maximally robust control synthesis under unknown disturbances. We consider a general nonlinear system, subject to a Signal Temporal Logic (STL) specification, and wish to jointly synthesize the maximal possible disturbance bounds and the corresponding controllers that ensure the STL specification is satisfied under these bounds. Many works have considered STL satisfaction under given bounded disturbances. Yet, to the authors' best knowledge, this is the first work that aims to maximize the permissible disturbance set and find the corresponding controllers that ensure satisfying the STL specification with maximum disturbance robustness. We extend the notion of disturbance-robust semantics for STL, which is a property of a specification, dynamical system, and controller, and provide an algorithm to get the maximal disturbance robust controllers satisfying an STL specification using Hamilton-Jacobi reachability. We show its soundness and provide a simulation example with an Autonomous Underwater Vehicle (AUV). Copyright © 2024, The Authors. All rights reserved.

关键词： Controllers

Navigating Users in Emotion-Aware Conversational Agents: Exploring Strategies for Enhanced Interaction

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SSRN

SSRN 2025年

作者： Zhang, Yuchong Ma, Yong Fu, Di Portales, Stephanie Zubicueta Fjeld, Morten Kragic, Danica KTH Royal Institute of Technology Stockholm Sweden University of Bergen Bergen Norway University of Surrey Guildford United Kingdom Norwegian University of Science and Technology Trondheim Norway Chalmers University of Technology Gothenburg Sweden Department of Robotics Perception and Learning

Conversational agents (CAs) are increasingly embedded in daily life, yet their ability to navigate user emotions efficiently is still evolving. This study investigates how users with varying traits – gender, personality, and cultural background (ethnicity and geographic location) – adapt their interaction strategies with emotion-aware CAs in specific emotional scenarios. Using an emotion-aware CA prototype expressing five distinct emotions (neutral, happy, sad, angry, and fear ) through male and female voices, we examine how interaction dynamics shift across different voices and emotional contexts through empirical studies. Our findings reveal distinct variations in user engagement and conversational strategies based on individual traits, emphasizing the value of personalized, emotion-sensitive interactions. By analysing both qualitative and quantitative data, we demonstrate that tailoring CAs to user characteristics can enhance user satisfaction and interaction quality. This work underscores the critical need for ongoing research to design CAs that not only recognise but also adaptively respond to emotional needs, ultimately supporting a diverse user groups more effectively. © 2025, The Authors. All rights reserved.

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Guided Decoding for Robot On-line Motion Generation and Adaption

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Guided Decoding for Robot On-line Motion Generation and Adap...

IEEE-RAS International Conference on Humanoid Robots

作者： Nutan Chen Botond Cseke Elie Aljalbout Alexandros Paraschos Marvin Alles Patrick van der Smagt Machine Learning Research Lab Volkswagen Group Germany Department of Informatics Robotics and Perception Group University of Zurich (UZH) Department of Neuroinformatics UZH and ETH Zurich Switzerland Faculty of Informatics Eötvös Loránd University Budapest Hungary

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

We present a novel motion generation approach for robot arms, with high degrees of freedom, in complex settings that can adapt online to obstacles or new via points. learning from Demonstration facilitates rapid adaptation to new tasks and optimizes the utilization of accumulated expertise by allowing robots to learn and generalize from demonstrated trajectories. We train a transformer architecture, based on conditional variational autoencoder, on a large dataset of simulated trajectories used as demonstrations. Our architecture learns essential motion generation skills from these demonstrations and is able to adapt them to meet auxiliary tasks. Additionally, our approach implements auto-regressive motion generation to enable real-time adaptations, as, for example, introducing or changing via-points, and velocity and acceleration constraints. Using beam search, we present a method for further adaption of our motion generator to avoid obstacles. We show that our model successfully generates motion from different initial and target points and that is capable of generating trajectories that navigate complex tasks across different robotic platforms.

关键词： Robot motion Visualization Robot kinematics Humanoid robots Color Transformers Robustness Trajectory Decoding Collision avoidance

CITR: A Coordinate-Invariant Task Representation for Robotic Manipulation

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CITR: A Coordinate-Invariant Task Representation for Robotic...

IEEE International Conference on robotics and Automation (ICRA)

作者： Peter So Rafael I. Cabral Muchacho Robin Jeanne Kirschner Abdalla Swikir Luis Figueredo Fares J. Abu-Dakka Sami Haddadin Munich Institute of Robotics and Machine Intelligence TUM Germany Division of Robotics Perception and Learning KTH Royal Institute of Technology Sweden Department of Electrical and Electronic Engineering Omar Al-Mukhtar University (OMU) Libya School of Computer Science University of Nottingham UK Electronic and Informatics Department Faculty of Engineering Mondragon Unibertsitatea Spain

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The basis for robotics skill learning is an adequate representation of manipulation tasks based on their physical properties. As manipulation tasks are inherently invariant to the choice of reference frame, an ideal task representation would also exhibit this property. Nevertheless, most robotic learning approaches use unprocessed, coordinate-dependent robot state data for learning new skills, thus inducing challenges regarding the interpretability and transferability of the learned *** this paper, we propose a transformation from spatial measurements to a coordinate-invariant feature space, based on the pairwise inner product of the input measurements. We describe and mathematically deduce the concept, establish the task fingerprints as an intuitive image-based representation, experimentally collect task fingerprints, and demonstrate the usage of the representation for task classification. This representation motivates further research on data-efficient and transferable learning methods for online manipulation task classification and task-level perception.

关键词： learning systems Coordinate measuring machines Robot kinematics Taxonomy Fingerprint recognition Mathematical models Libraries

Participatory Design of AI with Children: Reflections on IDC Design Challenge

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arXiv 2023年

作者： Bai, Zhen Judd, Frances Polinsky, Naomi Yadollahi, Elmira Department of Computer Science University of Rochester United States Bennett Day School United States Department of Psychology Northwestern University United States Division of Robotics Perception and Learning KTH Royal Institute of Technology Sweden

Children growing up in the era of Artificial Intelligence (AI) will be most impacted by the technology across their life span. Participatory Design (PD) is widely adopted by the Interaction Design and Children (IDC) community, which empowers children to bring their interests, needs, and creativity to the design process of future technologies. While PD has drawn increasing attention to human-centered AI design, it remains largely untapped in facilitating the design process of AI technologies relevant to children and their communities. In this paper, we report intruiguing children’s design ideas on AI technologies resulting from the "Research and Design Challenge" of the 22nd ACM Interaction Design and Children conference (IDC 2023). The diversity of design problems, AI applications and capabilities revealed by the children’s design ideas shed light on the potential of engaging children in PD activities for future AI technologies and education. We discuss opportunities and challenges for accessible and inclusive PD experiences with children. © 2023, CC BY-NC-ND.

关键词： Engineering education

Unfolding the Literature: A Review of Robotic Cloth Manipulation

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arXiv 2024年

作者： Longhini, Alberta Wang, Yufei Garcia-Camacho, Irene Blanco-Mulero, David Moletta, Marco Welle, Michael Alenyà, Guillem Yin, Hang Erickson, Zackory Held, David Borràs, Júlia Kragic, Danica Department of Robotics Perception and Learning KTH Royal Institute of Technology Stockholm100 44 Sweden The Robotics Institute Carnegie Mellon University PittsburghPA15213 United States Institut de Robòtica i Informàtica Industrial CSIC-UPC Barcelona08028 Spain Department of Computer Science University of Copenhagen Copenhagen2100 Denmark

The realm of textiles spans clothing, households, healthcare, sports, and industrial applications. The deformable nature of these objects poses unique challenges that prior work on rigid objects cannot fully address. The increasing interest within the community in textile perception and manipulation has led to new methods that aim to address challenges in modeling, perception, and control, resulting in significant progress. However, this progress is often tailored to one specific textile or a subcategory of these textiles. To understand what restricts these methods and hinders current approaches from generalizing to a broader range of real-world textiles, this review provides an overview of the field, focusing specifically on how and to what extent textile variations are addressed in modeling, perception, benchmarking, and manipulation of textiles. We finally conclude by identifying key open problems and outlining grand challenges that will drive future advancements in the field. Copyright © 2024, The Authors. All rights reserved.

关键词： Textiles

HRI Wasn’t Built In a Day: A Call To Action For Responsible HRI Research

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HRI Wasn’t Built In a Day: A Call To Action For Responsible...

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Micol Spitale Rebecca Stower Maria Teresa Parreira Elmira Yadollahi Iolanda Leite Hatice Gunes Department of Computer Science and Technology University of Cambridge UK Department of Electronic Information and Bio-Engineering Politecnico di Milano Italy Division of Robotics Perception and Learning KTH Royal Institute of Technology Information Science Cornell University School of Computing and Communications Lancaster University UK

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

In recent years, the awareness of the academy around responsible research has notably increased. For instance, with advances in machine learning and artificial intelligence, recent efforts have been made to promote ethical, fair, and inclusive AI and robotics. To better understand if and to what extent HRI is incentivizing researchers to engage in responsible research, we conducted an exploratory review of the publishing guidelines for the most popular HRI conference venues. We identified 18 conferences which published at least 7 HRI papers in 2022. From these, we discuss four themes relevant to conducting responsible HRI research in line with the Responsible Research and Innovation framework: ethical and human participant considerations, transparency and reproducibility, accessibility and inclusion, and plagiarism and LLM use. We identify several gaps and room for improvement within HRI regarding responsible research. Finally, we establish a call to action to provoke conversations among HRI researchers about the importance of conducting responsible research within emerging fields like HRI.

关键词： Ethics Technological innovation Reviews Rail to rail inputs Human-robot interaction Oral communication Reproducibility of results Stakeholders Robots Guidelines