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arXiv 2024年

作者： Ding, Haoran Jaquier, Noémie Peters, Jan Rozo, Leonel Division of Robotics Perception and Learning KTH Royal Institute of Technology Stockholm Sweden Bosch Center for Artificial Intelligence Renningen Germany Computer Science Department The Technische Universität Darmstadt Darmstadt Germany

Diffusion-based visuomotor policies excel at learning complex robotic tasks by effectively combining visual data with high-dimensional, multi-modal action distributions. However, diffusion models often suffer from slow inference due to costly denoising processes or require complex sequential training arising from recent distilling approaches. This paper introduces Riemannian Flow Matching Policy (RFMP), a model that inherits the easy training and fast inference capabilities of flow matching (FM). Moreover, RFMP inherently incorporates geometric constraints commonly found in realistic robotic applications, as the robot state resides on a Riemannian manifold. To enhance the robustness of RFMP, we propose Stable RFMP (SRFMP), which leverages LaSalle’s invariance principle to equip the dynamics of FM with stability to the support of a target Riemannian distribution. Rigorous evaluation on eight simulated and real-world tasks show that RFMP successfully learns and synthesizes complex sensorimotor policies on Euclidean and Riemannian spaces with efficient training and inference phases, outperforming Diffusion Policies while remaining competitive with Consistency Policies. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Network Parameter Control in Cellular Networks through Graph-Based Multi-Agent Constrained Reinforcement learning

Network Parameter Control in Cellular Networks through Graph...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Albin Larsson Forsberg Alexandros Nikou Aneta Vulgarakis Feljan Jana Tumova Ericsson Research Research Area Artificial Intelligence (AI) Stockholm Sweden Department of Electrical Engineering and Computer Science Division of Robotics Perception and Learning KTH Stockholm Sweden

Cellular networks are growing in complexity at increasing speed and the geographical locations in which they are deployed in are getting denser. Traditional control methods fall short in providing a scalable and dynamic way of adapting the network to new conditions. Distributed multiagent reinforcement learning successfully addresses scalability problems seen in centralized approaches. The question of achieving learning with constraint satisfaction in distributed systems is still left unanswered in the state-of-the-art. In this work, we aim to perform distributed multi-agent constrained reinforcement learning in order to learn a policy online while satisfying imposed constraints. We use a coordination graph to model the interactions between agents and decompose the global value function. A conservative safety critic is trained in parallel to evaluate the safety of proposed actions. Our method allows for separate training of both the critic and the value network independently of each other, and hence offers flexibility in how and when to train the different models. The results are compared to a baseline using no safety critic. Simulations show that the agents succeed in learning a policy that can satisfy the constraints, while still maximizing the objective.

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Generating Scenarios from High-Level Specifications for Object Rearrangement Tasks

Generating Scenarios from High-Level Specifications for Obje...

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

作者： Sanne van Waveren Christian Pek Iolanda Leite Jana Tumova Danica Kragic School of Interactive Computing Georgia Institute of Technology Atlanta GA USA Department of Cognitive Robotics Delft University of Technology Delft the Netherlands Division of Robotics Perception and Learning KTH Royal Institute of Technology Stockholm Sweden

Rearranging objects is an essential skill for robots. To quickly teach robots new rearrangements tasks, we would like to generate training scenarios from high-level specifications that define the relative placement of objects for the task at hand. Ideally, to guide the robot's learning we also want to be able to rank these scenarios according to their difficulty. Prior work has shown how generating diverse scenario from specifications and providing the robot with easy-to-difficult samples can improve the learning. Yet, existing scenario generation methods typically cannot generate diverse scenarios while controlling their difficulty. We address this challenge by conditioning generative models on spatial logic specifications to generate spatially-structured scenarios that meet the specification and desired difficulty level. Our experiments showed that generative models are more effective and data-efficient than rejection sam-pling and that the spatially-structured scenarios can drastically improve training of downstream tasks by orders of magnitude.

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

arXiv

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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

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Ice-Breakers, Turn-Takers and Fun-Makers: Exploring Robots for Groups with Teenagers

arXiv

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arXiv 2025年

作者： Gillet, Sarah Winkle, Katie Belgiovine, Giulia Leite, Iolanda Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden CONTACT unit Istituto Italiano di Tecnologia DIBRIS department Università degli Studi di Genova Italy

Successful, enjoyable group interactions are important in public and personal contexts, especially for teenagers whose peer groups are important for self-identity and self-esteem. Social robots seemingly have the potential to positively shape group interactions, but it seems difficult to effect such impact by designing robot behaviors solely based on related (human interaction) literature. In this article, we take a user-centered approach to explore how teenagers envisage a social robot "group assistant". We engaged 16 teenagers in focus groups, interviews, and robot testing to capture their views and reflections about robots for groups. Over the course of a two-week summer school, participants co-designed the action space for such a robot and experienced working with/wizarding it for 10+ hours. This experience further altered and deepened their insights into using robots as group assistants. We report results regarding teenagers’ views on the applicability and use of a robot group assistant, how these expectations evolved throughout the study, and their repeat interactions with the robot. Our results indicate that each group moves on a spectrum of need for the robot, reflected in use of the robot more (or less) for ice-breaking, turn-taking, and fun-making as the situation demanded. © 2025, CC BY-NC-ND.

关键词： Social robots

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Robust STL Control Synthesis under Maximal Disturbance Sets

Robust STL Control Synthesis under Maximal Disturbance Sets

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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

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Robust STL Control Synthesis under Maximal Disturbance Sets

arXiv

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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

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

SSRN

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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

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

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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

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CITR: A Coordinate-Invariant Task Representation for Robotic Manipulation

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

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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

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