Psychiatric symptom identification on social media aims to infer fine-grained mental health symptoms from user-generated posts, allowing a detailed understanding of users' mental states. However, the construction of large-scale symptom-level datasets remains challenging due to the resource-intensive nature of expert labeling and the lack of standardized annotation guidelines, which in turn limits the generalizability of models to identify diverse symptom expressions from user-generated text. To address these issues, we propose SynSym, a synthetic data generation framework for constructing generalizable datasets for symptom identification. Leveraging large language models (LLMs), SynSym constructs high-quality training samples by (1) expanding each symptom into sub-concepts to enhance the diversity of generated expressions, (2) producing synthetic expressions that reflect psychiatric symptoms in diverse linguistic styles, and (3) composing realistic multi-symptom expressions, informed by clinical co-occurrence patterns. We validate SynSym on three benchmark datasets covering different styles of depressive symptom expression. Experimental results demonstrate that models trained solely on the synthetic data generated by SynSym perform comparably to those trained on real data, and benefit further from additional fine-tuning with real data. These findings underscore the potential of synthetic data as an alternative resource to real-world annotations in psychiatric symptom modeling, and SynSym serves as a practical framework for generating clinically relevant and realistic symptom expressions.

Vision sensors are extensively used for localizing a robot's pose, particularly in environments where global localization tools such as GPS or motion capture systems are unavailable. In many visual navigation systems, localization is achieved by detecting and tracking visual features or landmarks, which provide information about the sensor's relative pose. For reliable feature tracking and accurate pose estimation, it is crucial to maintain visibility of a sufficient number of features. This requirement can sometimes conflict with the robot's overall task objective. In this paper, we approach it as a constrained control problem. By leveraging the invariance properties of visibility constraints within the robot's kinematic model, we propose a real-time safety filter based on quadratic programming. This filter takes a reference velocity command as input and produces a modified velocity that minimally deviates from the reference while ensuring the information score from the currently visible features remains above a user-specified threshold. Numerical simulations demonstrate that the proposed safety filter preserves the invariance condition and ensures the visibility of more features than the required minimum. We also validated its real-world performance by integrating it into a visual simultaneous localization and mapping (SLAM) algorithm, where it maintained high estimation quality in challenging environments, outperforming a simple tracking controller.

Aerial physical interaction (APhI) with a multirotor-based platform such as pushing a heavy object demands generation of a sufficiently large interaction force while maintaining the stability. Such requirement can cause rotor saturation, because the rotor thrust enlarged for interaction force may leave a reduced margin for attitude stabilization. We first design an H-shaped coaxial tiltrotor that can generate a sufficiently large interaction force than a conventional multirotor. We then propose an overall framework composed of high-level robust controller and low-level control allocation for the coaxial tiltrotor to ensure robustness against uncertain motion of the unknown interacting object and to overcome the saturation issue. To guarantee the robustness at all time, we design a controller based on a nonlinear disturbance observer (DOB). Then, we formulate a problem of computing low-level actuator inputs avoiding rotor saturation as a tractable nonlinear optimization problem, which can be solved real-time. The proposed framework is validated in extensive real-world experiments where the 3.3 kg tiltrotor successfully pushes a cart weighing up to 60 kg. An ablation study with the tiltrotor shows effectiveness of the proposed control allocation law in avoiding rotor saturation. Furthermore, a comparative experiment with a conventional multirotor shows failure in the same setting, which validates the use of the coaxial tiltrotor. An experimental video can be found athttps://youtu.be/Gdmcmoz_UjUNote to Practitioners—The motivation of this work is to enable heavy object manipulation, especially pushing operation, using a multirotor platform. To push a heavy object, which has not been treated in existing works, is challenging due to the presence of a large unknown interaction wrench and rotor saturation issue. To resolve this problem, we first build an H-shaped coaxial tiltrotor that can effectively utilize rotor thrusts to generate horizontal interaction force. Then, a robust controller is designed to address uncertainty in the interaction wrench for the tiltrotor. Finally, we propose an optimization-based control allocation to avoid rotor saturation. Real-world experiments including heavy object pushing and ablation study with respect to the control allocation law are conducted to show the effectiveness of the proposed framework. We validate the use of the coaxial tiltrotor in additional comparative experiments with a conventional multirotor which fails in pushing a 60 kg cart. The proposed framework can be utilized in various applications such as disaster relief operations, emergency rescue and aerial delivery.

Safety verification in motion planning is known to be computationally burdensome, despite its importance in robotics. In this paper, we investigate the behavior of safe receding horizon motion planners when the update interval becomes infinitesimal. By requiring the trajectory parameters to evolve continuously in time, the trajectory optimization problem is reformulated into a time-derivative form, whose decision variables are their rate of change. This results in a quadratic programming problem which directly provides safe input, and can be regarded as a real-time safety filter. The input expressivity is also enhanced by leveraging the differentiable structure of the parameter space. The proposed safety filter is experimentally validated using a wheeled ground robot in obstacle-cluttered environments. The result shows that the safety filter is capable of generating safe inputs in real-time, while addressing hundreds of constraints simultaneously.

Aerial unperching of multirotors has received little attention as opposed to perching that has been investigated to elongate operation time. This study presents a new aerial robot capable of both perching and unperching autonomously on/from a ferromagnetic surface during flight, and a switching controller to avoid rotor saturation and mitigate overshoot during transition between free-flight and perching. To enable stable perching and unperching maneuvers on/from a vertical surface, a lightweight (≈ 1 kg), fully actuated tiltrotor that can hover at 90◦pitch angle is first developed. We design a perching/unperching module composed of a single servomotor and a magnet, which is then mounted on the tiltrotor. A switching controller including exclusive control modes for transitions between free-flight and perching is proposed. Lastly, we propose a simple yet effective strategy to ensure robust perching in the presence of measurement and control errors and avoid collisions with the perching site immediately after unperching. We validate the proposed framework in experiments where the tiltrotor successfully performs perching and unperching on/from a vertical surface during flight. We further show effectiveness of the proposed transition mode in the switching controller by ablation studies where large overshoot and even collision with a perching site occur. To the best of the authors’ knowledge, this work presents the first autonomous aerial unperching framework using a fully actuated tiltrotor.

This study presents a new hardware design and control of a minimally actuated 5 control degrees of freedom (CDoF) quadrotor-based tiltrotor. The proposed tiltrotor possesses several characteristics distinct from those found in existing works, including: 1) minimal number of actuators for 5 CDoF, 2) large margin to generate interaction force during aerial physical interaction (APhI), and 3) no mechanical obstruction in thrust direction rotation. Thanks to these properties, the proposed tiltrotor is suitable for perching-enabled APhI since it can hover parallel to an arbitrarily oriented surface and can freely adjust its thrust direction. To fully control the 5-CDoF of the designed tiltrotor, we construct an asymptotically stabilizing controller with stability analysis. The proposed tiltrotor design and controller are validated in experiments where the first two experiments of x, y position tracking and pitch tracking show controllability of the added CDoF compared to a conventional quadrotor. Finally, the last experiment of perching and cart pushing demonstrates the proposed tiltrotor's applicability to perching-enabled APhI.