In recent years, with the rapid advancement of space development, there has been increasing demand for constructing solar power generation systems on the Moon using modular robots. This study has been proposing a modular construction method aimed at building tall structures on the lunar surface. Their modules adopt a truncated octahedron shape one of a space-filling polyhedron allowing their configuration and placement to be determined geometrically by exploiting its space-filling property. This approach can also be treated as a space quantization and establishes a unified platform in which module development, configuration, and positioning can be derived with comparatively simple calculations. In this paper, we present the results of a field experiment using newly prototyped modules, along with the previously proposed construction method and module design. The results demonstrate the feasibility of the proposed method and highlight the potential for space quantization for robot motions based on space-filling properties.
In self-supervised robot learning, data is acquired through active interaction with the environment, which is costly. Therefore, sample-efficient exploration is vital. To this end, intrinsic motivation (IM) methods such as learning progress (LP) have been adopted in robot learning, with variable success across tasks; whereas in machine learning, active learning (AL) is considered the go-to solution, especially for classification tasks. However, there is no systematic method for fusing both approaches for continuous regression tasks encountered in robot learning. To this end, we propose MUSEL (Model Uncertainty for Sample-Efficient Learning), a novel AL framework tailored for regression tasks in robotics, such as action-effect prediction. MUSEL introduces a novel model uncertainty metric that combines total predictive uncertainty, learning progress, and input diversity to guide experience gathering. We choose Stochastic Variational Deep Kernel Learning (SVDKL) as the base learning model and validate our approach by showing its efficacy in effect prediction tasks where a manipulator robot interacts with objects on a confined tabletop. Experiments comparing MUSEL with strong baselines show that MUSEL improves learning accuracy and sample efficiency. Overall, this study offers MUSEL as an effective online learning model applicable to any robot self-learning task where experience gathering is costly.
With the aim of mitigating the impacts caused by variations in the rainy season that directly affect the production of forage grasses in San Lucas, Michoacán, this work presents the design and evaluation of a direct-solar pumping system for drip irrigation over an area of 0.74 ha. This development considered the meteorological data from the National Renewable Energy Laboratory database, as well as the hydraulic characteristics of the plot and the water requirements of the crop, in order to design both the hydraulic infrastructure and the photovoltaic array. The proposal was evaluated using the specialized software, System Advisor Model, resulting in a system capable of supplying a required flow rate of 3.52 lps during the dry season, ensuring continuous drip irrigation operation, without the use of battery banks, from 10:00 AM. to 4:00 PM. The obtained results support the technical and economic feasibility of the system, therefore, its implementation is recommended as a cost-effective and sustainable alternative for local agricultural production.
Cockpit-style exoskeleton systems are a promising approach for the whole-body teleoperation of humanoid robots, yet they face two major challenges. The first is the difficulty of intuitive torso posture control, arising from the operator’s seated and constrained posture. The second is the lack of transparent feedback for kinematic singularities, which do not manifest as physical forces. This paper proposes two novel methods to address these respective challenges: 1) an indirect torso control interface that converts the operator’s foot- actuated torque into the robot’s torso angular velocity, and 2) a haptic feedback scheme that guides the operator’s arm away from singularities based on the gradient of the arm’s manipulability measure. We implemented these methods into a teleoperation system and successfully demonstrated tasks such as picking up an object from the floor and an operator’s active avoidance of singular postures.
Work-related musculoskeletal disorders (WMSDs) persist in material-handling jobs where lifting, twisting, and carrying induce high, localized muscle demands. This paper presents a sensor-driven framework that (i) detects biomechanical strain from surface electromyography (sEMG) and (ii) quantifies the impact of a passive back-support exoskeleton during industrially relevant tasks. With data from 20 participants performing standardized tasks with and without the device, we introduce a data-driven strain labeling method that replaces ad-hoc thresholds with piecewise linear regression to identify individualized strain onset. A compact deep neural network handles severe class imbalance via SMOTE and decision-threshold optimization, yielding 83.5% overall accuracy and a macro-averaged F1-score of 0.70 for binary strain classification. Muscle-specific analyses reveal significant reductions in biceps and oblique activation (p < 0.001) alongside compensatory increases in erector spinae and lower-limb activity, indicating load redistribution rather than uniform offloading. The result is a scalable, real-time approach that captures both when strain begins and how effort shifts across muscle groups, capabilities that traditional peak-sEMG or subjective assessments miss. By uniting wearable sensing, automated strain onset detection, and imbalance-aware learning, this work advances objective, continuous, and human-centered ergonomic monitoring and provides actionable evidence for the deployment of passive exoskeletons in smart industrial environments.
