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IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： Mohamad Reza Shahabian Alashti Mohammad Hossein Bamorovat Abadi Patrick Holthaus Catherine Menon Farshid Amirabdollahian Robotics Research Group School of Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Human activity recognition (HAR) plays a critical role in diverse applications and domains, from assessments of ambient assistive living (AAL) settings and the development of smart environments to human-robot interaction (HRI) scenarios. However, using mobile robot cameras in such contexts has limitations like restricted field of view and possible noise. Therefore, employing additional fixed cameras can enhance the field of view and reduce susceptibility to noise. Never-theless, integrating additional camera perspectives increases complexity, a concern exacerbated by the number of real-time processes that robots should perform in the AAL scenario. This paper introduces our methodology that facilitates the combination of multiple views and compares different aspects of fusing information at low, medium and high levels. Their comparison is guided by parameters such as the number of training parameters, floating-point operations per second (FLOPs), training time, and accuracy. Our findings uncover a paradigm shift, challenging conventional beliefs by demonstrating that simplistic CNN models outperform their more complex counterparts using this innovation. Additionally, the pivotal role of pipeline and data combination emerges as a crucial factor in achieving better accuracy levels. In this study, integrating the additional view with the Robot-view resulted in an accuracy increase of up to 25 %. Ultimately, we have successfully attained a streamlined and efficient multi-view HAR pipeline, which will now be incorporated into AAL interaction scenarios.

关键词： Training Technological innovation Accuracy Robot vision systems Pipelines Noise Human-robot interaction Cameras Real-time systems Human activity recognition

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Curriculum-based Sample Efficient Reinforcement Learning for Robust Stabilization of a Quadrotor

arXiv

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arXiv 2025年

作者： Mauricio, Fausto Suarez, Lagos Saradagi, Akshit Sumathy, Vidya Kotpalliwar, Shruti Nikolakopoulos, George Robotics and AI group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden

This article introduces a curriculum learning approach to develop a reinforcement learning-based robust stabilizing controller for a Quadrotor that meets predefined performance criteria. The learning objective is to achieve desired positions from random initial conditions while adhering to both transient and steady-state performance specifications. This objective is challenging for conventional one-stage end-to-end reinforcement learning, due to the strong coupling between position and orientation dynamics, the complexity in designing and tuning the reward function, and poor sample efficiency, which necessitates substantial computational resources and leads to extended convergence times. To address these challenges, this work decomposes the learning objective into a three-stage curriculum that incrementally increases task complexity. The curriculum begins with learning to achieve stable hovering from a fixed initial condition, followed by progressively introducing randomization in initial positions, orientations and velocities. A novel additive reward function is proposed, to incorporate transient and steady-state performance specifications. The results demonstrate that the Proximal Policy Optimization (PPO)based curriculum learning approach, coupled with the proposed reward structure, achieves superior performance compared to a single-stage PPO-trained policy with the same reward function, while significantly reducing computational resource requirements and convergence time. The curriculum-trained policy’s performance and robustness are thoroughly validated under random initial conditions and in the presence of disturbances. © 2025, CC BY.

关键词： Curricula

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Learning Generalizable Manipulation Policy with Adapter-Based Parameter Fine-Tuning

Learning Generalizable Manipulation Policy with Adapter-Base...

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

作者： Kai Lu Kim Tien Ly William Hebberd Kaichen Zhou Ioannis Havoutis Andrew Markham Department of Computer Science University of Oxford Oxford Robotics Institute University of Oxford Oxford UK

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This study investigates the use of adapters in reinforcement learning for robotic skill generalization across multiple robots and tasks. Traditional methods are typically reliant on robot-specific retraining and face challenges such as efficiency and adaptability, particularly when scaling to robots with varying kinematics. We propose an alternative approach where a disembodied (virtual) hand manipulator learns a task (i.e., an abstract skill) and then transfers it to various robots with different kinematic constraints without retraining the entire model (i.e., the concrete, physical implementation of the skill). Whilst adapters are commonly used in other domains with strong supervision available, we show how weaker feedback from robotic control can be used to optimize task execution by preserving the abstract skill dynamics whilst adapting to new robotic domains. We demonstrate the effectiveness of our method with experiments conducted in the SAPIEN ManiSkill environment, showing improvements in generalization and task success rates. All code, data, and additional videos are at this GitHub link: https://***/genrob.

关键词： Training Hands Adaptation models Kinematics Reinforcement learning Manipulators Robots Intelligent robots Videos Software development management

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APriCoT: Action Primitives based on Contact-state Transition for In-Hand Tool Manipulation

APriCoT: Action Primitives based on Contact-state Transition...

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IEEE-RAS International Conference on Humanoid Robots

作者： Daichi Saito Atsushi Kanehira Kazuhiro Sasabuchi Naoki Wake Jun Takamatsu Hideki Koike Katsushi Ikeuchi Department of Computer Science Institute of Science Tokyo Tokyo Japan Applied Robotics Research Microsoft Redmond WA USA

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

In-hand tool manipulation is an operation that not only manipulates a tool within the hand (i.e., in-hand manipulation) but also achieves a grasp suitable for a task after the manipulation. This study aims to achieve an in-hand tool manipulation skill through deep reinforcement learning. The difficulty of learning the skill arises because this manipulation requires (A) exploring long-term contact-state changes to achieve the desired grasp and (B) highly-varied motions depending on the contact-state transition. (A) leads to a sparsity of a reward on a successful grasp, and (B) requires an RL agent to explore widely within the state-action space to learn highly-varied actions, leading to sample inefficiency. To address these issues, this study proposes Action Primitives based on Contact-state Transition (APriCoT). APriCoT decomposes the manipulation into short-term action primitives by describing the operation as a contact-state transition based on three action representations (detach, crossover, attach). In each action primitive, fingers are required to perform short-term and similar actions. By training a policy for each primitive, we can mitigate the issues from (A) and (B). This study focuses on a fundamental operation as an example of in-hand tool manipulation: rotating an elongated object grasped with a precision grasp by half a turn to achieve the initial grasp. Experimental results demonstrated that ours succeeded in both the rotation and the achievement of the desired grasp, unlike existing studies. Additionally, it was found that the policy was robust to changes in object shape.

关键词： Training Shape Humanoid robots Deep reinforcement learning

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On the Faithfulness of Vision Transformer Explanations

On the Faithfulness of Vision Transformer Explanations

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Junyi Wu Weitai Kang Hao Tang Yuan Hong Yan Yan Department of Computer Science Illinois Institute of Technology USA Robotics Institute Carnegie Mellon University USA Department of Computer Science University of Connecticut USA

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

To interpret Vision Transformers, post-hoc explanations assign salience scores to input pixels, providing human-understandable heatmaps. However, whether these inter-pretations reflect true rationales behind the model's output is still underexplored. To address this gap, we study the faithfulness criterion of explanations: the assigned salience scores should represent the influence of the corresponding input pixels on the model's predictions. To evaluate faithful-ness, we introduce Salience-guided Faithfulness Coefficient (SaCo), a novel evaluation metric leveraging essential in-formation of salience distribution. Specifically, we con-duct pair-wise comparisons among distinct pixel groups and then aggregate the differences in their salience scores, resulting in a coefficient that indicates the explanation's degree of faithfulness. Our explorations reveal that cur-rent metrics struggle to differentiate between advanced ex-planation methods and Random Attribution, thereby failing to capture the faithfulness property. In contrast, our pro-posed SaCo offers a reliable faithfulness measurement, es-tablishing a robust metric for interpretations. Furthermore, our SaCo demonstrates that the use of gradient and multi-layer aggregation can markedly enhance the faithfulness of attention-based explanation, shedding light on potential paths for advancing Vision Transformer explainability.

关键词： Measurement Heating systems computer vision Correlation Aggregates Predictive models Benchmark testing

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Development and Evaluation of a Transparency Model for the Design of Humanoid Service Robots

Development and Evaluation of a Transparency Model for the D...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Lejla Nukovic Jérôme Kirchhoff Oskar von Stryk Department of Computer Science Simulation Systems Optimization and Robotics Group Technical University of Darmstadt Germany

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

The seamless integration and acceptance of humanoid service robots in society requires a general understanding of those robots. An overall transparent design can be a means to establish this. To guide the implementation of effective transparency mechanisms, we propose the novel Transparency Design Model, which builds on the concept of Interaction Transparency from our previous work. This model encompasses a wide range of transparency aspects aimed to improve the overall transparency of humanoid service robot designs. To demonstrate the applicability of our model and gather initial insights on its impact on robot perception, we conducted a user study involving 50 participants. The study compared a neutral and transparent robot behavior within a complex sales service scenario. Our findings indicate that the transparent design significantly improve the robot’s likeability, anthropomorphism, and benevolence. Additionally, we observed positive trends in attractiveness, stimulation, and novelty as measured by the User Experience Questionnaire. These measurable effects highlight the important role of Interaction Transparency. The results also imply that a holistic system analysis in multiple complex and application-dependent interactions is worthwhile as the service content influences the perception and effects of the individual design steps. Further, there is a need to develop a new scale for measuring Interaction Transparency. Lastly, future research could benefit from employing multi-modal assessment tools.

关键词： Service robots Atmospheric measurements Standards organizations Humanoid robots Particle measurements Market research User experience Reliability Anthropomorphism System analysis and design

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Visual-Language Collaborative Representation Network for Broad-Domain Few-Shot Image Classification 24

Visual-Language Collaborative Representation Network for Bro...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Guo, Qianyu Ren, Jieji Wang, Haofen Wu, Tianxing Ge, Weifeng Zhang, Wenqiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China College of Design and Innovation Tongji University Shanghai China School of Computer Science and Engineering Southeast University Nanjing China School of Computer Science Fudan University Shanghai China Engineering Research Center of AI & Robotics Ministry of Education Academy for Engineering & Technology Fudan University Shanghai China

ISBN: (纸本)9798400706868

Visual-language models based on CLIP have shown remarkable abilities in general few-shot image classification. However, their performance drops in specialized fields such as healthcare or agriculture, because CLIP's pre-training does not cover all category data. Existing methods excessively depend on the multi-modal information representation and alignment capabilities acquired from CLIP pre-training, which hinders accurate generalization to unfamiliar domains. To address this issue, this paper introduces a novel visual-language collaborative representation network (MCRNet), aiming at acquiring a generalized capability for collaborative fusion and representation of multi-modal information. Specifically, MCRNet learns to generate relational matrices from an information fusion perspective to acquire aligned multi-modal features. This relationship generation strategy is category-agnostic, so it can be generalized to new domains. A class-adaptive fine-tuning inference technique is also introduced to help MCRNet efficiently learn alignment knowledge for new categories using limited data. Additionally, the paper establishes a new broad-domain few-shot image classification benchmark containing seven evaluation datasets from five domains. Comparative experiments demonstrate that MCRNet outperforms current state-of-the-art models, achieving an average improvement of 13.06% and 13.73% in the 1-shot and 5-shot settings, highlighting the superior performance and applicability of MCRNet across various domains. © 2024 ACM.

关键词： Visual languages

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Resiliency in Space Autonomy: a Review

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Current robotics Reports 2023年第1期4卷 1-12页

作者： Banerjee, Avijit Mukherjee, Moumita Satpute, Sumeet Nikolakopoulos, George Robotics and AI Department of Computer science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

The article provides an extensive overview on the resilient autonomy advances made across various missions, orbital or deep-space, that captures the current research approaches while investigating the possible future direction of resiliency in space autonomy. In recent years, the need for several automated operations in space applications has been rising, that ranges from the following: spacecraft proximity operations, navigation and some station keeping applications, entry, decent and landing, planetary surface exploration, etc. Also, with the rise of miniaturization concepts in spacecraft, advanced missions with multiple spacecraft platforms introduce more complex behaviours and interactions within the agents, which drives the need for higher levels of autonomy and accommodating collaborative behaviour coupled with robustness to counter unforeseen uncertainties. This collective behaviour is now referred to as resiliency in autonomy. As space missions are getting more and more complex, for example applications where a platform physically interacts with non-cooperative space objects (debris) or planetary bodies coupled with hostile, unpredictable, and extreme environments, there is a rising need for resilient autonomy solutions. Resilience with its key attributes of robustness, redundancy and resourcefulness will lead toward new and enhanced mission paradigms of space missions.

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Constant-time Motion Planning with Anytime Refinement for Manipulation

Constant-time Motion Planning with Anytime Refinement for Ma...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Itamar Mishani Hayden Feddock Maxim Likhachev School of Computer Science Robotics Institute Carnegie Mellon University Pittsburgh PA Electrical and Computer Engineering Department Pittsburgh University

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Robotic manipulators are essential for future autonomous systems, yet limited trust in their autonomy has confined them to rigid, task-specific systems. The intricate configuration space of manipulators, coupled with the challenges of obstacle avoidance and constraint satisfaction, often makes motion planning the bottleneck for achieving reliable and adaptable autonomy. Recently, a class of constant-time motion planners (CTMP) was introduced. These planners employ a preprocessing phase to compute data structures that enable online planning provably guarantee the ability to generate motion plans, potentially sub-optimal, within a user defined time bound. This framework has been demonstrated to be effective in a number of time-critical tasks. However, robotic systems often have more time allotted for planning than the online portion of CTMP requires, time that can be used to improve the solution. To this end, we propose an anytime refinement approach that works in combination with CTMP algorithms. Our proposed framework, as it operates as a constant time algorithm, rapidly generates an initial solution within a user-defined time threshold. Furthermore, functioning as an anytime algorithm, it iteratively refines the solution’s quality within the allocated time budget. This enables our approach to strike a balance between guaranteed fast plan generation and the pursuit of optimization over time. We support our approach by elucidating its analytical properties, showing the convergence of the anytime component towards optimal solutions. Additionally, we provide empirical validation through simulation and real-world demonstrations on a 6 degree-of-freedom robot manipulator, applied to an assembly domain.

关键词： Manipulators Reliability engineering Data structures Real-time systems Planning Time factors Task analysis

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Preprocessing-based Kinodynamic Motion Planning Framework for Intercepting Projectiles using a Robot Manipulator

Preprocessing-based Kinodynamic Motion Planning Framework fo...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Ramkumar Natarajan Hanlan Yang Qintong Xie Yash Oza Manash Pratim Das Fahad Islam Muhammad Suhail Saleem Howie Choset Maxim Likhachev The Robotics Institute School of Computer Science Carnegie Mellon University USA Department of Engineering Science University of Oxford UK

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

We are interested in studying sports with robots and starting with the problem of intercepting a projectile moving toward a robot manipulator equipped with a shield. To successfully perform this task, the robot needs to (i) detect the incoming projectile, (ii) predict the projectile’s future motion, (iii) plan a minimum-time rapid trajectory that can evade obstacles and intercept the projectile, and (iv) execute the planned trajectory. These four steps must be performed under the manipulator’s dynamic limits and extreme time constraints (≤ 350ms in our setting) to successfully intercept the projectile. In addition, we want these trajectories to be smooth to reduce the robot’s joint torques and the impulse on the platform on which it is mounted. To this end, we propose a kinodynamic motion planning framework that preprocesses smooth trajectories offline to allow real-time collision-free executions online. We present an end-to-end pipeline along with our planning framework, including perception, prediction, and execution modules. We evaluate our framework experimentally in simulation and show that it has a higher blocking success rate than the baselines. Further, we deploy our pipeline on a robotic system comprising an industrial arm (ABB IRB-1600) and an onboard stereo camera (ZED 2i), which achieves a 78% success rate in projectile interceptions.

关键词： Service robots Projectiles Pipelines Robot vision systems Trajectory Planning Time factors

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