Frontiers in Robotics and AI最新文献

Mini cranes play a pivotal role in construction due to their versatility across numerous scenarios. Recent advancements in Reinforcement Learning (RL) have enabled agents to operate cranes in virtual environments for predetermined tasks, paving the way for future real-world deployment. Traditionally, most RL agents use a squashed Gaussian distribution to select actions. In this study, we investigate a mini-crane scenario that could potentially be fully automated by AI and explore replacing the Gaussian distribution with the Kumaraswamy distribution, a close relative of the Beta distribution, for action stochastic selection. Our results indicate that the Kumaraswamy distribution offers computational advantages while maintaining robust performance, making it an attractive alternative for RL applications in continuous control applications.

In response to the growing need for flexibility in handling complex tasks, research on human-robot collaboration (HRC) has garnered considerable attention. Recent studies on HRC have achieved smooth handover tasks between humans and robots by adaptively responding to human states. Collaboration was further improved by conveying the state of the robot to humans via robotic interactive motion cues. However, in scenarios such as collaborative assembly tasks that require precise positioning, methods relying on motion or forces caused by interactions through the shared object compromise both task accuracy and smoothness, and are therefore not directly applicable. To address this, the present study proposes a method to convey the stiffness of the robot to a human arm during collaborative human-robot assembly tasks in a manner that does not affect the shared object or task, aiming to enhance efficiency and reduce human workload. Sixteen participants performed a collaborative assembly task with a robot, which involved unscrewing, repositioning, and reattaching a part while the robot held and adjusted the position of the part. The experiment examined the effectiveness of the proposed method, in which the robot's stiffness was communicated to a participant's forearm. The independent variable, tested within-subjects, was the stiffness presentation method, with three levels: without the proposed method (no presentation) and with the proposed method (real-time and predictive presentations). The results demonstrated that the proposed method enhanced task efficiency by shortening task completion time, which was associated with lower subjective workload scores.

Despite the growing interest in Artificial Intelligence (AI) for pediatric rehabilitation, family engagement in the technologies design remains limited. Understanding how AI-driven tools align with family needs, caregiving routines, and ethical concerns is crucial for their successful adoption. In this study, we actively involved nine families of children with Cerebral Palsy (CP) in an online participatory design workshop, underscoring both the feasibility and the need of integrating family's perspectives into AI development. Families enthusiastically participated, not only sharing insights but also appreciating the opportunity to contribute to shaping future technologies. Their active engagement challenges the assumption that co-design with families is complex or impractical, highlighting how structured yet flexible methodologies can make such crucial initiatives highly effective. The online format further facilitated participation, allowing families to join the discussion and ensuring a diverse range of perspectives. The workshop's key findings reveal three core priorities for families: 1. AI should adapt to daily caregiving routines rather than impose rigid structures; 2. digital tools should enhance communication and collaboration between families and clinicians, rather than replace human interaction; and 3. AI-driven systems could empower children's autonomy while maintaining parental oversight. Additionally, families raised critical concerns about data privacy, transparency, and the need to preserve empathy in AI-mediated care. Our findings reinforce the urgent need to shift toward family-centered AI design, moving beyond purely technological solutions toward ethically responsible, inclusive innovations. This research not only demonstrates the possibility and success of engaging families in co-design processes but also provides a model for future AI development that genuinely reflects the lived experiences of children and caregivers.

This paper addresses the challenge of enabling robots to autonomously prepare meals by bridging natural language recipe instructions and robotic action execution. We propose a novel methodology leveraging Actionable Knowledge Graphs to map recipe instructions into six core categories of robotic manipulation tasks, termed Action Cores cutting, pouring, mixing, preparing, pick and place, and cook and cool. Each AC is subdivided into Action Groups which represent a specific motion parameterization required for task execution. Using the Recipe1M + dataset (Marín et al., IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43, 187-203), encompassing over one million recipes, we systematically analysed action verbs and matched them to ACs by using direct matching and cosine similarity, achieving a coverage of 76.5%. For the unmatched verbs, we employ a neuro-symbolic approach, matching verbs to existing AGs or generating new action cores utilizing a Large Language Model Our findings highlight the versatility of AKGs in adapting general plans to specific robotic tasks, validated through an experimental application in a meal preparation scenario. This work sets a foundation for adaptive robotic systems capable of performing a wide array of complex culinary tasks with minimal human intervention.

Introduction: Animal-involved scenarios pose significant challenges for autonomous driving systems due to their rarity, unpredictability, and safety-critical nature. Despite their importance, existing vision-language datasets for autonomous driving largely overlook these long-tail situations.

Methods: To address this gap, we introduce AniDriveQA, a novel visual question answering (VQA) dataset specifically designed to evaluate vision-language models (VLMs) in driving scenarios involving animals. The dataset is constructed through a scalable pipeline that collects diverse animal-related traffic scenes from internet videos, filters and annotates them using object detection and scene classification models, and generates multi-task VQA labels with a large vision-language model. AniDriveQA includes three key task types: scene description, animal description, and driving suggestion.

Results: For evaluation, a hybrid scheme was employed that combined classification accuracy for structured tasks with LLM-based scoring for open-ended responses. Extensive experiments on various open-source VLMs revealed large performance disparities across models and task types.

Discussion: The experimental results demonstrate that AniDriveQA effectively exposes the limitations of current VLMs in rare yet safety-critical autonomous driving scenarios. The dataset provides a valuable diagnostic benchmark for advancing reasoning, perception, and decision-making capabilities in future vision-language models.

Limited battery capacity poses a challenge for autonomous robots. We believe that instead of relying solely on electric motors and batteries, basically Conventional Autonomous Robots (CAR), one way to address this challenge may be to develop Biohybrid Autonomous Robots (BAR), based on current achievements of the field of biohybrid robotics. The BAR approach is based on the facts that fat store high amount of energy, that biological muscles generate decent force per unit of cross-sectional area and that biological muscles have capability for regeneration and adaptation compared to electric motors. To reach conclusions about the feasibility of BAR, this study uses data from the fields of muscle energetics, robotics, engineering, physiology, biomechanics and others to perform analysis and interdisciplinary calculations. Our calculations show that the BAR approach is up to 5.1 times more efficient in terms of the mass of energy substrate to useful energy transported than the Conventional Autonomous Robots (CAR) with mass-produced batteries in an ideal scenario. The study also presents the model for determining the point of the rational use of the BAR, taking into the account basal metabolism of living systems. The results of this study provide a preliminary basis for further research of the BAR, putting it into the context of the other possible solutions for energy autonomy problem: Generator-Powered Autonomous Robots (GPAR) and Fuell-Cell Autonomous Robots (FCAR).

This paper presents a robust vision-based motion planning framework for dual-arm manipulators that introduces a novel three-way force equilibrium with velocity-dependent stabilization. The framework combines an improved Artificial Potential Field (iAPF) for linear velocity control with a Proportional-Derivative (PD) controller for angular velocity, creating a hybrid twist command for precise manipulation. A priority-based state machine enables human-like asymmetric dual-arm manipulation. Lyapunov stability analysis proves the asymptotic convergence to desired configurations. The method introduces a computationally efficient continuous distance calculation between links based on line segment configurations, enabling real-time collision monitoring. Experimental validation integrates a real-time vision system using YOLOv8 OBB that achieves 20 frames per second with 0.99/0.97 detection accuracy for bolts/nuts. Comparative tests against traditional APF methods demonstrate that the proposed approach provides stabilized motion planning with smoother trajectories and optimized spatial separation, effectively preventing inter-arm collisions during industrial component sorting.

Musculoskeletal disorders, particularly low back pain, are some of the most common occupational health issues globally, causing significant personal suffering and economic burdens. Workers performing repetitive manual material handling tasks are especially at risk. FleXo, a lightweight (1.35 kg), flexible, ergonomic, and passive back-support exoskeleton is intended to reduce lower back strain during lifting tasks while allowing full freedom of movement for activities like walking, sitting, or side bending. FleXo's design results from an advanced multi-objective design optimization approach that balances functionality and user comfort. In this work, validated through user feedback in a series of relevant repetitive tasks, it is demonstrated that FleXo can reduce the perceived physical effort during lifting tasks, enhance user satisfaction, improve employee wellbeing, promote workplace safety, decrease injuries, and lower the costs (both to society and companies) associated with lower back pain and injury.