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Guided Decoding for Robot On-line Motion Generation and Adaption 23

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

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

作者： Chen, Nutan Cseke, Botond Aljalbout, Elie Paraschos, Alexandros Alles, Marvin Van Der Smagt, Patrick Machine Learning Research Lab Volkswagen Group Germany Robotics and Perception Group Department of Informatics Switzerland Uzh Eth Zurich Department of Neuroinformatics Switzerland Eötvös Loránd University Faculty of Informatics Budapest Hungary

ISBN: (纸本)9798350373578

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. © 2024 IEEE.

关键词： Trajectories

Robust STL Control Synthesis under Maximal Disturbance Sets 63

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Robust STL Control Synthesis under Maximal Disturbance Sets

63rd IEEE Conference on Decision and Control, CDC 2024

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

ISBN: (纸本)9798350316339

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). © 2024 IEEE.

关键词： Control system synthesis

Articulating Mechanical Sympathy for Somaesthetic Human–Machine Relations

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Articulating Mechanical Sympathy for Somaesthetic Human–Mac...

2024 ACM Designing Interactive Systems Conference, DIS 2024

作者： La Delfa, Joseph Garrett, Rachael Lampinen, Airi Höök, Kristina Bitcraze Malmö Sweden Robotics Perception and Learning KTH Royal Institute of Technology Stockholm Sweden Media Technology and Interaction Design KTH Royal Institute of Technology Stockholm Sweden Department of Computer and Systems Sciences Stockholm University Stockholm Sweden

ISBN: (纸本)9798400705830

We present mechanical sympathy as a generative design concept for cultivating somaesthetic relationships with machines and machine-like systems. We identify the qualities of mechanical sympathy using the design case of How to Train your Drone (HTTYD), a unique human-drone research product designed to explore the process by which people discover and co-create the somaesthetic potential of drones. We articulate the qualities – (i) machine-agency, (ii) oscillations, and (iii) aesthetic pursuits – by using descriptive and refective accounts of our design strategies and of our co-creators engaging with the system. We also discuss how each quality can extend soma design research;conceptualizing of appreciative, temporal, and idiosyncratic relationships with machines that can complement technical learning and enrich human-machine interaction. Finally, we ground our concept in a similar selection of works from across the HCI community. © 2024 Copyright held by the owner/author(s).

关键词： Drones

Human-Aligned Image Models Improve Visual Decoding from the Brain

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arXiv 2025年

作者： Rajabi, Nona Ribeiro, Antônio H. Vasco, Miguel Taleb, Farzaneh Björkman, Mårten Kragic, Danica Division of Robotics Perception and Learning KTH Royal Institute of Technology Stockholm Sweden Department of Information Technology Uppsala University Uppsala Sweden

Decoding visual images from brain activity has significant potential for advancing brain-computer interaction and enhancing the understanding of human perception. Recent approaches align the representation spaces of images and brain activity to enable visual decoding. In this paper, we introduce the use of human-aligned image encoders to map brain signals to images. We hypothesize that these models more effectively capture perceptual attributes associated with the rapid visual stimuli presentations commonly used in visual brain data recording experiments. Our empirical results support this hypothesis, demonstrating that this simple modification improves image retrieval accuracy by up to 21% compared to state-of-the-art methods. Comprehensive experiments confirm consistent performance improvements across diverse EEG architectures, image encoders, alignment methods, participants, and brain imaging modalities. © 2025, CC BY.

关键词： Image coding

FAST-LIVO2 on Resource-Constrained Platforms: LiDAR-Inertial-Visual Odometry with Efficient Memory and Computation

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arXiv 2025年

作者： Zhou, Bingyang Zheng, Chunran Wang, Ziming Zhu, Fangcheng Cai, Yixi Zhang, Fu Laboratory Department of Mechanical Engineering University of Hong Kong Hong Kong Division of Robotics Perception and Learning KTH Royal Institute of Technology Stockholm Sweden

This paper presents a lightweight LiDAR-inertial-visual odometry system optimized for resource-constrained platforms. It integrates a degeneration-aware adaptive visual frame selector into error-state iterated Kalman filter (ESIKF) with sequential updates, improving computation efficiency significantly while maintaining a similar level of robustness. Additionally, a memory-efficient mapping structure combining a locally unified visual-LiDAR map and a long-term visual map achieves a good trade-off between performance and memory usage. Extensive experiments on x86 and ARM platforms demonstrate the system’s robustness and efficiency. On the Hilti dataset, our system achieves a 33% reduction in per-frame runtime and 47% lower memory usage compared to FAST-LIVO2, with only a 3 cm increase in RMSE. Despite this slight accuracy trade-off, our system remains competitive, outperforming state-of-the-art (SOTA) LIO methods such as FAST-LIO2 and most existing LIVO systems. These results validate the system’s capability for scalable deployment on resource-constrained edge computing platforms. Copyright © 2025, The Authors. All rights reserved.

关键词： Kalman filters

Raising Body Ownership in End-to-End Visuomotor Policy learning via Robot-Centric Pooling

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Raising Body Ownership in End-to-End Visuomotor Policy Learn...

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

作者： Zheyu Zhuang Ville Kyrki Danica Kragic Robotics Perception and Learning Lab EECS KTH Royal Institute of Technology Stockholm Sweden Department of Electrical Engineering and Automation (EEA) Intelligent Robotics Group Aalto University Espoo Finland

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

We present Robot-centric Pooling (RcP), a novel pooling method designed to enhance end-to-end visuomo-tor policies by enabling differentiation between the robots and similar entities or their surroundings. Given an image-proprioception pair, RcP guides the aggregation of image features by highlighting image regions correlating with the robot’s proprioceptive states, thereby extracting robot-centric image representations for policy learning. Leveraging contrastive learning techniques, RcP integrates seamlessly with existing visuomotor policy learning frameworks and is trained jointly with the policy using the same dataset, requiring no extra data collection involving self-distractors. We evaluate the proposed method with reaching tasks in both simulated and real-world settings. The results demonstrate that RcP significantly enhances the policies’ robustness against various unseen distractors, including self-distractors, positioned at different locations. Additionally, the inherent robot-centric characteristic of RcP enables the learnt policy to be far more resilient to aggressive pixel shifts compared to the baselines. Code available at: https://***/Zheyu-Zhuang/RcP

关键词： Propioception Performance gain Image representation Data collection Feature extraction Robustness History Robots Intelligent robots Resilience

Relative Representations: Topological and Geometric Perspectives

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arXiv 2024年

作者： García-Castellanos, Alejandro Marchetti, Giovanni Luca Kragic, Danica Scolamiero, Martina Amsterdam Machine Learning Lab University of Amsterdam Netherlands Department of Mathematics KTH Royal Institute of Technology Sweden Division of Robotics Perception and Learning KTH Royal Institute of Technology Sweden

Relative representations are an established approach to zero-shot model stitching, consisting of a non-trainable transformation of the latent space of a deep neural network. Based on insights of topological and geometric nature, we propose two improvements to relative representations. First, we introduce a normalization procedure in the relative transformation, resulting in invariance to non-isotropic rescalings and permutations. The latter coincides with the symmetries in parameter space induced by common activation functions. Second, we propose to deploy topological densification when fine-tuning relative representations, a topological regularization loss encouraging clustering within classes. We provide an empirical investigation on a natural language task, where both the proposed variations yield improved performance on zero-shot model stitching. © 2024, CC BY.

关键词： Topology

Stable Autonomous Flow Matching

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arXiv 2024年

作者： Sprague, Christopher Iliffe Elofsson, Arne Azizpour, Hossein Department of Biochemistry and Biophysics Science for Life Laboratory Stockholm University Solna Sweden Department of Robotics Perception and Learning KTH Royal Institute of Technology Stockholm Sweden

In contexts where data samples represent a physically stable state, it is often assumed that the data points represent the local minima of an energy landscape. In control theory, it is well-known that energy can serve as an effective Lyapunov function. Despite this, connections between control theory and generative models in the literature are sparse, even though there are several machine learning applications with physically stable data points. In this paper, we focus on such data and a recent class of deep generative models called flow matching. We apply tools of stochastic stability for time-independent systems to flow matching models. In doing so, we characterize the space of flow matching models that are amenable to this treatment, as well as draw connections to other control theory principles. We demonstrate our theoretical results on two examples. Copyright © 2024, The Authors. All rights reserved.

关键词： Control theory

What’s at Stake? Robot explanations matter for high but not low-stake scenarios

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What’s at Stake? Robot explanations matter for high but not...

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Gaspar Isaac Melsion Rebecca Stower Katie Winkle Iolanda Leite Division of Robotics Perception and Learning KTH Royal Institute of Technology Stockholm Sweden Department of Information Technology Uppsala University Sweden

Although the field of Explainable Artificial Intelligence (XAI) in Human-Robot Interaction is gathering increasing attention, how well different explanations compare across HRI scenarios is still not well understood. We conducted an exploratory online study with 335 participants analysing the interaction between type of explanation (counterfactual, feature-based, and no explanation), the stake of the scenario (high, low) and the application scenario (healthcare, industry). Participants viewed one of 12 different vignettes depicting a combination of these three factors and rated their system understanding and trust in the robot. Compared to no explanation, both counterfactual and feature-based explanations improved system understanding and performance trust (but not moral trust). Additionally, when no explanation was present, high-stake scenarios led to significantly worse performance trust and system understanding. These findings suggest that explanations can be used to calibrate users’ perceptions of the robot in high-stake scenarios.

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