Frontiers in Robotics and AI最新文献

The rapid loss of biodiversity worldwide is unprecedented, with more species facing extinction now than at any other time in human history. Key factors contributing to this decline include habitat destruction, overexploitation, and climate change. There is an urgent need for innovative and effective conservation practices that leverage advanced technologies, such as autonomous drones, to monitor wildlife, manage human-wildlife conflicts, and protect endangered species. While drones have shown promise in conservation efforts, significant technological challenges remain, particularly in developing reliable, cost-effective solutions capable of operating in remote, unstructured, and open-ended environments. This paper explores the technological advancements necessary for deploying autonomous drones in nature conservation and presents the interdisciplinary scientific methodology of the WildDrone doctoral network as a basis for integrating research in drones, computer vision, and machine learning for ecological monitoring. We report preliminary results demonstrating the potential of these technologies to enhance biodiversity conservation efforts. Based on our preliminary findings, we expect that drones and computer vision will develop to further automate time consuming observational tasks in nature conservation, thus allowing human workers to ground conservation actions on evidence based on large and frequent data.

In the era of artificial intelligence and rapidly advancing robotics, the field of Human-Robot Interaction (HRI) has taken center stage across multiple domains, including psychology. From a psychological perspective, it is therefore essential to deepen our understanding of the factors that shape the quality of these interactions and their implications. This emphasis also aligns with the principles of Industry 5.0, which prioritize human well-being and use technologies to promote sustainable progress. The present study employs an exploratory mixed-method approach and aims to examine perceptions of warmth, competence and discomfort with the Furhat social robot in a psychological assessment setting. Specifically, we investigated young adults' interactions with the Furhat social robot while it administered the Depression, Anxiety and Stress Scale (DASS-21). Following the interaction, the participants completed the short version of the Robot Social Attributes Scale (RoSAS-SF) to assess perceived warmth, competence and discomfort, and provided qualitative feedback regarding their interactional experiences and acceptance of the robot. The findings provide preliminary insights into the respondents' perceptions of and openness toward robot-administered psychological screening, suggesting that the Furhat social robot may have potential as an assistive tool in mental health assessment contexts. These results highlight the need for further research with larger samples to examine the role of social robots in psychological practice more comprehensively.

Objective: Pilot study with ten healthy adults, testing whether a lightweight, low-cost knee orthosis equipped with EMG-driven impedance control reduces quadriceps muscle effort during the sit-to-stand (STS) transition.

Methods: Ten able-bodied adults performed 15 paced STS repetitions under three conditions: without orthosis (No-Ortho), orthosis worn unpowered (Ortho-OFF; friction-compensated), and orthosis actively powered (Ortho-ON). Surface electromyography (EMG) was recorded using 8-channel thigh bracelets on both legs. EMG signals from the braced leg were processed using ridge regression and slew-rate limiting to generate a normalized control signal that dynamically scales knee stiffness while maintaining constant damping. Median values and trial-to-trial variance of the average rectified EMG (ARV) were analyzed across four distinct movement phases (SIT, UP, STAND, DOWN) using linear mixed-effects models with log-transformed data and Bonferroni-adjusted planned contrasts.

Results: Powered assistance significantly reduced median bilateral ARV by 11% during the UP phase and 15% during the DOWN phase $\left(p_{\mathrm{adj}}<0.001\right)$ , with greater reductions (up to 21%) observed on the braced limb. Variance in muscle activation decreased substantially (by up to 44%) on the braced leg during the DOWN phase, suggesting more repeatable activation patterns and neuromuscular consistency across trials. No significant compensatory activation was observed in the contralateral limb. Additionally, within-session adaptation trends were observed as participants progressively increased preparatory torque during the SIT phase, while UP-phase ARV trended downward.

Conclusion: A lightweight, affordable knee orthosis employing a rapid ( $\approx$ 10 s), minimally calibrated EMG-driven impedance controller effectively reduces quadriceps muscle activation during STS without compromising natural movement coordination. Torque capacity limitations (16 Nm) may limit effectiveness for heavier users, and further research is needed to evaluate kinematic fidelity fully.

Robotic fleet management systems are increasingly vital for sustainable operations in agriculture, forestry, and other field domains where labor shortages, efficiency, and environmental concerns intersect. We present FORMIGA, a fleet management framework that integrates human operators and autonomous robots into a collaborative ecosystem. FORMIGA combines standardised communication through the Robot Operating System with a user-centered interface for monitoring and intervention, while also leveraging large language models to generate executable task code from natural language prompts. The framework was deployed and validated within the FEROX project, a European initiative addressing sustainable berry harvesting in remote environments. In simulation-based trials, FORMIGA demonstrated adaptive task allocation, reduced operator workload, and faster task completion compared to semi-autonomous control, enabling dynamic labor division between humans and robots. By enhancing productivity, supporting worker safety, and promoting resource-efficient operations, FORMIGA contributes to the economic, and environmental dimensions of sustainability, offering a transferable tool for advancing human-robot collaboration in field robotics.

This paper presents a comparative study of data-driven acceleration techniques for mixed-integer bilinear programs (MIBLPs) applied to robot motion planning. MIBLPs combine discrete decision variables and nonlinear constraints, making them computationally challenging for real-time robotics applications. We investigate two reformulation strategies: (1) converting binary variables into continuous variables with complementarity constraints (MPCC), and (2) converting bilinear constraints into mixed-integer linear constraints using McCormick envelopes (MICP). Using offline computed solutions as datasets, we apply K-nearest neighbor methods to warm-start both reformulations. We experimented with the proposed data-driven MIBLP formulation for motion planning on a linear inverted pendulum with contacts, and planning motion using a single rigid body model with mode transitions and contacts. Our results demonstrate that when sufficient data is available, MICP achieves consistently fast solving speeds that are suitable for real-time computation, while MPCC achieves higher success rates with limited amount of data. Our approach is capable of planning motions for the SCALER robot platform to transition between bipedal and quadrupedal configurations to navigate around obstacles without pre-specified gaits. Code for reproducing our results is available at https://github.com/XuanLin/MIBLP_benchmark.

The path planning capability of autonomous robots in complex environments is crucial for their widespread application in the real world. However, long-term decision-making and sparse reward signals pose significant challenges to traditional reinforcement learning (RL) algorithms. Offline hierarchical reinforcement learning offers an effective approach by decomposing tasks into two stages: high-level subgoal generation and low-level subgoal attainment. Advanced Offline HRL methods, such as Guider and HIQL, typically introduce latent spaces in high-level policies to represent subgoals, thereby handling high-dimensional states and enhancing generalization. However, these approaches require the high-level policy to search and generate sub-objectives within a continuous latent space. This remains a complex and sample-inefficient challenge for policy optimization algorithms-particularly policy gradient-based PPO-often leading to unstable training and slow convergence. To address this core limitation, this paper proposes a novel offline hierarchical PPO framework-LG-H-PPO (Latent Graph-based Hierarchical PPO). The core innovation of LG-H-PPO lies in discretizing the continuous latent space into a structured "latent graph." By transforming high-level planning from challenging "continuous creation" to simple "discrete selection," LG-H-PPO substantially reduces the learning difficulty for the high-level policy. Preliminary experiments on standard D4RL offline navigation benchmarks demonstrate that LG-H-PPO achieves significant advantages over advanced baselines like Guider and HIQL in both convergence speed and final task success rates. The main contribution of this paper is introducing graph structures into latent variable HRL planning. This effectively simplifies the action space for high-level policies, enhancing the training efficiency and stability of offline HRL algorithms for long-sequence navigation tasks. It lays the foundation for future offline HRL research combining latent variable representations with explicit graph planning.

Study design: Prospective study.

Objectives: This study aimed to evaluate the accuracy and safety of robot-assisted anterior transpedicular screw (ATPS) fixation in human cervical spine specimens.

Methods: A spine robotic system was used to implant thirty-six 1.2 mm Kirschner wires (K-wires) into the cervical pedicles (C4-C7) of five human specimens. Accuracy was assessed by comparing the planned trajectories with the actual K-wire positions. The Gertzbein-Robbins classification system (GRS), adapted for cervical pedicles, was used to evaluate accuracy; Grades A and B (<2 mm pedicle breach) were considered clinically acceptable. Secondary metrics included entry point and angle offsets.

Results: Of the 36 K-wires implanted, nine were placed in C4 and C6, 10 in C5, and eight in C7. According to the adapted GRS, 25 placements (69.4%) were Grade A, 10 (27.8%) were Grade B, and one was Grade C, resulting in a 97.2% clinically acceptable placement rate. The mean target offset was 2.29 ± 1.72 mm, the entry offset was 2.47 ± 1.57 mm, and the angle offset was 5.67° ± 3.72°. No significant differences were observed between the left and right sides (p > 0.05).

Conclusion: Robot-assisted ATPS fixation in cervical specimens achieved high accuracy with 97.2% of placements rated clinically acceptable, indicating its technical feasibility and potential utility in anterior cervical procedures.

For individuals with unilateral transtibial amputation, powered ankle-foot prostheses have the potential to reduce the metabolic rate of walking, which could contribute to improvements in mobility and quality of life; however, physiological improvements have not been consistently demonstrated in experimental studies. To improve our understanding of the biomechanical mechanisms that drive metabolic rate outcomes, we used a machine learning approach to model the relationship between multimodal biomechanical factors and the metabolic rate of walking with a powered ankle-foot prosthesis. Our model included 50 features describing spatiotemporal parameters, step-to-step transition work, joint kinematics, muscle activity, ground reaction forces, prosthesis settings, and subject characteristics, and resulted in a pseudo-R² of 0.986. Accumulated local effects plots were used to visualize the direction and magnitude of the relationship between each feature and the metabolic rate of walking. The features with the largest effect on metabolic rate were peak unaffected side ankle inversion angle, leading affected leg positive work during the step-to-step transition, and peak affected knee extension angle. This work furthers our knowledge about the biomechanical and physiological response to powered ankle-foot prosthesis use and could assist in developing new strategies to drive reductions in metabolic rate.