Frontiers in Robotics and AI最新文献

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.

Learning from human feedback is a popular approach to train robots to adapt to user preferences and improve safety. Existing approaches typically consider a single querying (interaction) format when seeking human feedback and do not leverage multiple modes of user interaction with a robot. We examine how to learn a penalty function associated with unsafe behaviors using multiple forms of human feedback, by optimizing both the query state and feedback format. Our proposed adaptive feedback selection is an iterative, two-phase approach which first selects critical states for querying, and then uses information gain to select a feedback format for querying across the sampled critical states. The feedback format selection also accounts for the cost and probability of receiving feedback in a certain format. Our experiments in simulation demonstrate the sample efficiency of our approach in learning to avoid undesirable behaviors. The results of our user study with a physical robot highlight the practicality and effectiveness of adaptive feedback selection in seeking informative, user-aligned feedback that accelerate learning. Experiment videos, code and supplementary materials are found on our website: https://tinyurl.com/AFS-learning.

Mine emergencies demand rapid and informed decision-making under extreme conditions, often placing personnel in life-threatening situations. Robotic assistance offers the potential to reduce unnecessary human exposure during such operations. This study examines the specific informational needs and communication preferences of mine rescue personnel for designing robotic systems for underground emergency response. A semi-structured interview was developed and conducted with ten mine rescue personnel and subject matter experts (SMEs). Responses were analyzed using thematic analysis and compared with established cognitive models to derive key design recommendations. Drawing on both field experience and hypothetical rescue scenarios, participants provided insights into key functional aspects of robotic systems, including mapping and navigation, gas detection and environmental monitoring, communication capabilities, system reliability, control, and the robot's specific roles during operations. The qualitative data was transcribed and analyzed to identify recurring themes and critical user guidelines. The findings revealed insights into the informational and interface recommendations of rescue teams, particularly the need for real-time situational data and customizable human-robot interfaces tailored to emergency scenarios. These results expose key deficiencies in the current human-robot interaction systems and offer actionable guidance for designing robotic technologies that better align with the operational needs of experienced responders. The outcomes of this study can serve as practical guidelines for developing effective interfaces to support underground mine rescue missions.

Morphological computation (MC)-the idea that body mechanics contribute to computation-has been widely explored in robotics and examined in humans from a physiological perspective. In this study, we report a behavioral pattern consistent with MC under temporal uncertainty. This proof-of-concept single-subject study examined whether human motor control shows behavioral signatures consistent with MC within a temporal-preparation paradigm. One participant completed 160 trials across four entropy levels (0, 1.0, 1.5, 2.0 bits) in two tasks: a low-embodiment button-pressing movement and a high-embodiment reaching movement. The reaching movement tended to show decreasing response variability (coefficient of variation, CV) with increasing temporal uncertainty, whereas the button-pressing movement tended to remain flat or slightly increase. Reaction time (RT) patterns also diverged: RTs tended to lengthen with longer foreperiods in the reaching condition but shortened in the button-pressing movement. Moreover, spatial accuracy in the reaching movement tended to improve across foreperiods. These adaptations emerged without explicit strategy instructions, may reflect sensitivity to temporal context. Taken together, these patterns appear consistent with MC-inspired accounts in which limb mechanics and modest co-contraction may filter temporal uncertainty rather than amplify it. Although constrained by a single-subject, four-level design, the findings offer preliminary evidence that is suggestive of embodied-intelligence principles that may generalize to human motor control, highlighting commonalities between biological and robotic systems in brain-body-environment dynamics.

Reproducibility is a particular challenge for soft robotics, yet remains a core part of its development and maturation as a field. This perspective dives into reproducibility: what it is, what it means, and how it can be applied to soft robotics. We first discuss reproducibility and delineate why it is a critical consideration for the field. Following this, our core contributions are in defining three moonshot goals that collectively chart a path towards a reproducible future for soft robotics. First, methods for testing and sharing data are discussed. Second, we show how testing procedures from other scientific disciplines can provide broad coverage over different types of soft robotics tests that we might want to complete. Finally, we highlight the need for methods to quantitatively compare the embodied intelligence that lies at the heart of soft robotics research. If successful, these steps would put the field in an excellent position to develop into the future.

This paper addresses the challenge of detecting and recovering from slip during robotic grasping of unknown objects, with the objective of establishing a robust no on-site or per-object calibration slip-recovery controller for an anthropomorphic hand. This hand is equipped with tri-axial piezoresistive tactile force sensors on each finger, and the proposed approach is validated through experimental analysis. The proposed methodology eliminates the need for object- or pose-specific calibration, explicit friction modelling, dense tactile arrays, line-of-sight vision, and a data-hungry learning process, enabling real-time implementation with minimal computation and integration effort. Using a commonly acquired online baseline from initial readings, slip is detected from relative changes between consecutive samples of the baseline-subtracted resultant tangential force, and object engagement is determined when the normal force reading deviates from a no-slip baseline beyond a preset threshold. Upon detecting slip, each finger increases its gripping force in closed-loop control until the slip stops, while enforcing motor-current protection in finger control to prevent actuator overload and object damage. Experiments were conducted on objects with different rigidity, weight, and surface textures, including an aluminium tube, a plastic water bottle, and a sponge. Additionally, the response time and variations in gripping force were evaluated. The results demonstrate rapid slip response via localized per-finger correction, good object conformability, and effective re-stabilization under different lifting speeds and sudden external disturbances. The per-finger design utilizes the minimum necessary correction at the offending finger, reducing unnecessary force increases on other fingers and improving grasp efficiency. This approach represents a practical solution for warehouse picking, human-robot collaboration, and in situ manipulation where task-specific calibrations, visual access, or training datasets are impractical.

Cloth unfolding and folding are fundamental tasks in autonomous robotic cloth manipulation as Physical AI. Driven by recent advances in deep learning, this area has developed rapidly in recent years. This review aims to systematically identify and summarize current progress in deep learning-based cloth unfolding and folding. Following the Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 41 relevant papers from 2019 to 2024 were selected for analysis. We examines various factors influencing cloth manipulation and find that, while current methods show impressive performance, several challenges remain unaddressed. These challenges include irregular cloth sizes and diverse initial garment states. Concerning datasets, there is a need for improved real-world data collection systems and more realistic cloth simulators, and the Sim2Real gap must be carefully considered. Additionally, the review highlights the importance of incorporating multi-modal sensors into current platforms and the emergence of novel primitive actions that enhance performance. The need for more consistent comparison metrics is emphasized, and strategies for addressing failure modes are discussed to further advance the field. From an algorithmic perspective, we reorganize existing learning methods into six learning and control paradigms: perception-guided heuristics, goal-conditioned manipulation policies, predictive and model-based state representation methods, reward-driven reinforcement learning over primitive actions, demonstration-driven skill transfer methods, and emerging large language model-based planning methods. We discuss how each paradigm contributes to unfolding and folding, their respective strengths and limitations, and the open problems that arise. Finally, we summarize the remaining challenges and provide future perspectives for physical AI.