Frontiers in Robotics and AI最新文献

Achieving robust, dexterous manipulation in unstructured environments remains a central challenge in robotics, particularly for continuous, contact-rich tasks like cleaning. While motion primitives can also be learned directly in full joint space, a compact, synergy-based representation provides a shared latent coordinate system that simplifies interpretation, modulation, and cross-task composition. We adopt a data-driven framework for representing and reproducing dexterous manipulation trajectories, using cleaning motions as a test bed. To model these movements, we combine Principal Component Analysis (PCA) with Probabilistic Movement Primitives (ProMPs), leveraging hand synergies. While the PCA and ProMP combination itself is established, our focus in this study, is on the cleaning use case and on the compositional generalization across tasks. PCA, applied in joint space, provides a compact, low-dimensional synergy space for coordinated finger movements, while the ProMPs encode the time-varying structure and variability of trajectories within this space. We first recorded a kinematic dataset of human cleaning motions with 20 degrees of freedom (DOF) haptic exoskeleton gloves across thirteen tasks and learn one ProMP per five selected training tasks in the PCA space. This dataset is then used as a basis to learn cleaning motions using the PCA + ProMPs. We demonstrate the ability of the learned primitives to reconstruct and reproduce kinematic patterns in simulation (Shadow Hand) and successfully deploy them on a physical robotic hand (Aeon Robotics). These results indicate that motion primitives, when grounded in synergy-informed coordinates, can generalize beyond grasping to encode and modulate contact-rich dexterous manipulation skills. Moreover, a library of the five task-specific ProMPs compositionally approximates trajectories from eight unseen cleaning tasks, with nearest-expert selection outperforming convex blends and Product-of-Experts combinations.

As artificial intelligence (AI) drives the development of next-generation robotic platforms and navigation systems that operate with increasing levels of autonomy in orthopedic and neurosurgical procedures, the methods by which human operators verify and validate these systems' operations become critically important. While significant effort has been spent on advancing technological capabilities and autonomy, comparatively little thought has been put into understanding how surgeons may effectively maintain oversight and assurance of these complex systems-despite retaining full legal and ethical responsibility for surgical outcomes. This mini-review synthesizes assurance mechanisms following the Sense-Think-Act framework: spatial intelligence (navigation and registration), cognitive assistance (AI-driven planning and adaptation), and physical operation (robot motion and force interaction). We highlight human-centered assurance as an opportunity to enable safe adoption of increasingly autonomous surgical systems. Finally, we outline essential research directions for developing assurance frameworks that scale with increasing autonomy while maintaining human responsibility and control in orthopedic and neurosurgical procedures.

ADMIRE (Analyzing Digitalized Human-Machine Interactions and Relationships) is a tool that was developed and tested as part of the Integrated Research Cluster. Its aim is to make explicit the implicit assumptions about humans and machines, as well as their potential and limitations. In this way, it provides a basis for structured, reflective research and development processes relating to human-machine interactions, as well as providing a starting point for ethical considerations in technology design. This article outlines the initial research and development approach and the insights gained from various research projects and application settings. We then trace this back to anthropology and the implicit images of humans and machines that determine the processes of research and development, and often prevent the implementation of 'technological solutions' to social problems. Here, we introduce the ADMIRE tool, along with its theoretical background and practical deployment. Finally, we reflect on the limitations of the tool itself and our experience to date.

Simulations of human Ia and Ib reflexes on a bio-inspired musculoskeletal robot driven by pneumatic artificial muscles (PAMs) offer a favorable option for counteracting disturbances in complex and dynamic work environments, providing a solution to the significant computational burdens that undermine its potential due to PAMs' inherent non-linearities. This research focuses on the simultaneous integration of human Ia and Ib reflexes (referred to as double-reflex) as countermeasures against physical disturbance in a musculoskeletal robot system driven by PAMs. The system's performance was examined, and implementation challenges were identified during experiments. Mechanisms were then applied to ensure the effective functioning of the integrated reflexes. Experimental results substantiated the effectiveness of the double-reflex system, highlighting its functionality within the robotic system. This investigation corroborates the viability of concurrently implementing Ia and Ib reflexes, providing a reference for the design of robotic reflex control systems. The study also offers some references based on the view of signal processing, regarding the possible functions of the human spinal cord that might be necessary to perform proper reflex actions.

This study develops a hierarchical learning and optimization framework that can learn and achieve well-coordinated multi-skill locomotion. The learned multi-skill policy can switch between skills automatically and naturally while tracking arbitrarily positioned goals and can recover from failures promptly. The proposed framework is composed of a deep reinforcement learning process and an optimization process. First, the contact pattern is incorporated into the reward terms to learn different types of gaits as separate policies without the need for any other references. Then, a higher-level policy is learned to generate weights for individual policies to compose multi-skill locomotion in a goal-tracking task setting. Skills are automatically and naturally switched according to the distance to the goal. The appropriate distances for skill switching are incorporated into the reward calculation for learning the high-level policy and are updated by an outer optimization loop as learning progresses. We first demonstrate successful multi-skill locomotion in comprehensive tasks on a simulated Unitree A1 quadruped robot. We also deploy the learned policy in the real world, showcasing trotting, bounding, galloping, and their natural transitions as the goal position changes. Moreover, the learned policy can react to unexpected failures at any time, perform prompt recovery, and successfully resume locomotion. Compared to baselines, our proposed approach achieves all the learned agile skills with improved learning performance, enabling smoother and more continuous skill transitions.

Introduction: This paper presents SimNav-XR, an extended reality platform that integrates XR technologies with modern robotics frameworks to support mobile robot simulation and development.

Methods: By connecting ROS2's communication infrastructure with Unity3D's rendering and XR capabilities through the ROS-TCP-Connector package, SimNav-XR provides a practical bridge between robotics middleware and game engine environments for visualization and testing. The platform implements components for physics-based robot modeling, LiDAR and IMU sensor simulation, environmental interaction dynamics, and XR interfaces supporting both Virtual Reality (VR) and Mixed Reality (MR) modes. These capabilities create interactive environments where developers can visualize and control simulated robots through immersive interfaces using the Meta Quest 3 headset with controller-based input.

Results: Experimental evaluations using established platforms (Turtlebot3 and ROSbotXL) demonstrate the framework's capabilities across virtual testing scenarios, showing successful autonomous navigation with obstacle avoidance and simultaneous localization and mapping (SLAM). The VR mode provides fully immersive virtual environments for development and testing, while the MR mode uses passthrough cameras to overlay virtual robots onto real-world surfaces via plane detection.

Discussion: XR visualization techniques provide insights into robot sensor data and navigation behavior, supporting robotics development and education through accessible simulation environments.

Background: Robotic-assisted therapy (RAT) has emerged as an effective approach to upper limb neurorehabilitation. Among available systems, the Armeo®Spring enables task-oriented, customizable training supported by virtual reality (VR), fostering motivation and neuroplasticity. This retrospective observational study aimed to evaluate longitudinal changes in performance across different VR exercises using Armeo®Spring session data from patients with diverse neurological conditions and to identify tasks exhibiting significant improvement within particular diagnoses, thereby supporting personalized robotic rehabilitation.

Methods: The dataset included adults (≥18) with common neurological disorders who completed ≥20 Armeo®Spring sessions using frequent integrated VR exercises. Performance across the first 20 sessions was analyzed using linear mixed-effects models with fixed effects for session, disease, age, sex, difficulty, and mechanical support, and random patient intercepts and slopes. False discovery rate (FDR) correction was applied to identify disease- and task-specific improvement trajectories.

Results: After sequential filtering, the final cohort included 71 patients (30 with ischemic stroke, 15 with hemorrhagic stroke, 15 with multiple sclerosis, and 11 with Parkinson's disease) who underwent rehabilitation using five different VR exercises: Balloons, Roll the Ball, Fly High-Elbow, The Goalkeeper, and Pirate Adventure. A significant improvement in Roll the Ball scores was detected for MS (slope = +9.41 points/session, FDR = 0.0015), IS (+9.18 points/session, FDR = 0.0001), and HS (+7.28 points/session, FDR = 0.023). In Fly High (Elbow), MS patients demonstrated a significant improvement (+6.84 points/session, FDR <0.001) as for IS patients (+5.00 points/session, FDR <0.001). Task difficulty was consistently correlated with lower scores across all games (FDR <0.05), whereas age and sex were not significant predictors in the adjusted models.

Conclusion: Disease-specific recovery profiles suggest that proximal, multi-joint VR exercises, such as Roll the Ball and Fly High (Elbow), may be particularly effective for patients with multiple sclerosis and ischemic stroke, whereas other exercises show smaller or non-significant improvements. These findings support tailoring VR-based rehabilitation to the patient's neurological condition, enabling targeted, condition-specific exercise selection and progression, which may enhance the effectiveness and efficiency of upper-limb recovery.