Solar energy is used for many mission-critical applications including space exploration, sensor systems to monitor wildfires, etc. Their operation can be limited or even terminated if solar panels are covered with dust or hit by space debris. To address this issue, we designed panel cleaning mechanisms and tested protective materials. For cleaning mechanisms, we designed and compared a wiper system and a rail system. For protective materials, we found through collision tests that polycarbonate was very promising, though the most important factor was layering a soft material between the panel's surface and a hard material. In the cleaning system comparisons, the wiper-based system was more efficient than the rail-based system in terms of cost, cleaning speed, and total power consumption.

A rubber duck that was stuck to a seaside rock for more than a year has proved the strength of a new sticky material. The adhesive could be used in deep-sea robots and repair work, or as surgical glue for medical procedures. "We developed a super-adhesive hydrogel that works extremely well even underwater – something very few materials can achieve," says Hailong Fan at Shenzhen University in China. Hydrogels are stretchy and soft materials. Fan, then at Hokkaido University in Japan, and his colleagues analysed 24,000 sticky protein sequences from many different organisms to identify the stickiest combinations of amino acids, the building blocks of proteins.

Soft robots, made from compliant materials, exhibit complex dynamics due to their flexibility and high degrees of freedom. Controlling soft robots presents significant challenges, particularly underactuation, where the number of inputs is fewer than the degrees of freedom. This research aims to develop a digital twin for multi-fingered soft grippers to advance the development of underactuation algorithms. The digital twin is designed to capture key effects observed in soft robots, such as nonlinearity, hysteresis, uncertainty, and time-varying phenomena, ensuring it closely replicates the behavior of a real-world soft gripper. Uncertainty is simulated using the Monte Carlo method. With the digital twin, a Q-learning algorithm is preliminarily applied to identify the optimal motion speed that minimizes uncertainty caused by the soft robots. Underactuated motions are successfully simulated within this environment. This digital twin paves the way for advanced machine learning algorithm training.

This paper presents Surena-V, a humanoid robot designed to enhance human-robot collaboration capabilities. The robot features a range of sensors, including barometric tactile sensors in its hands, to facilitate precise environmental interaction. This is demonstrated through an experiment showcasing the robot's ability to control a medical needle's movement through soft material. Surena-V's operational framework emphasizes stability and collaboration, employing various optimization-based control strategies such as Zero Moment Point (ZMP) modification through upper body movement and stepping. Notably, the robot's interaction with the environment is improved by detecting and interpreting external forces at their point of effect, allowing for more agile responses compared to methods that control overall balance based on external forces. The efficacy of this architecture is substantiated through an experiment illustrating the robot's collaboration with a human in moving a bar. This work contributes to the field of humanoid robotics by presenting a comprehensive system design and control architecture focused on human-robot collaboration and environmental adaptability.

It is desired to equip robots with the capability of interacting with various soft materials as they are ubiquitous in the real world. While physics simulations are one of the predominant methods for data collection and robot training, simulating soft materials presents considerable challenges. Specifically, it is significantly more costly than simulating rigid objects in terms of simulation speed and storage requirements. These limitations typically restrict the scope of studies on soft materials to small and bounded areas, thereby hindering the learning of skills in broader spaces. To address this issue, we introduce UBSoft, a new simulation platform designed to support unbounded soft environments for robot skill acquisition. Our platform utilizes spatially adaptive resolution scales, where simulation resolution dynamically adjusts based on proximity to active robotic agents. Our framework markedly reduces the demand for extensive storage space and computation costs required for large-scale scenarios involving soft materials. We also establish a set of benchmark tasks in our platform, including both locomotion and manipulation tasks, and conduct experiments to evaluate the efficacy of various reinforcement learning algorithms and trajectory optimization techniques, both gradient-based and sampling-based. Preliminary results indicate that sampling-based trajectory optimization generally achieves better results for obtaining one trajectory to solve the task. Additionally, we conduct experiments in real-world environments to demonstrate that advancements made in our UBSoft simulator could translate to improved robot interactions with large-scale soft material. More videos can be found at https://vis-www.cs.umass.edu/ubsoft/.

Performing long-term experimentation or large-scale data collection for machine learning in the field of soft robotics is challenging, due to the hardware robustness and experimental flexibility required. In this work, we propose a modular parallel robotic manipulation platform suitable for such large-scale data collection and compatible with various soft-robotic fabrication methods. Considering the computational and theoretical difficulty of replicating the high-fidelity, faster-than-real-time simulations that enable large-scale data collection in rigid robotic systems, a robust soft-robotic hardware platform becomes a high priority development task for the field. The platform's modules consist of a pair of off-the-shelf electrical motors which actuate a customizable finger consisting of a compliant parallel structure. The parallel mechanism of the finger can be as simple as a single 3D-printed urethane or molded silicone bulk structure, due to the motors being able to fully actuate a passive structure. This design flexibility allows experimentation with soft mechanism varied geometries, bulk properties and surface properties. Additionally, while the parallel mechanism does not require separate electronics or additional parts, these can be included, and it can be constructed using multi-functional soft materials to study compatible soft sensors and actuators in the learning process. In this work, we validate the platform's ability to be used for policy gradient reinforcement learning directly on hardware in a benchmark 2D manipulation task. We additionally demonstrate compatibility with multiple fingers and characterize the design constraints for compatible extensions.

Vertebrate animals benefit from a combination of rigidity for structural support and softness for adaptation. Similarly, integrating rigidity and softness can enhance the versatility of soft robotics. However, the challenges associated with creating durable bonding interfaces between soft and rigid materials have limited the development of hybrid robots. Existing solutions require specialized machinery, such as polyjet 3D printers, which are not commonly available. In response to these challenges, we have developed a 3D printing technique that can be used with almost all commercially available FDM printers. This technique leverages the common issue of underextrusion to create a strong bond between soft and rigid materials. Underextrusion generates a porous structure, similar to fibrous connective tissues, that provides a robust interface with the rigid part through layer fusion, while the porosity enables interlocking with the soft material. Our experiments demonstrated that this method outperforms conventional adhesives commonly used in soft robotics, achieving nearly 200\% of the bonding strength in both lap shear and peeling tests. Additionally, we investigated how different porosity levels affect bonding strength. We tested the technique under pressure scenarios critical to soft and hybrid robots and achieved three times more pressure than the current adhesion solution. Finally, we fabricated various hybrid robots using this technique to demonstrate the wide range of capabilities this approach and hybridity can bring to soft robotics. has context menu

In recent years, the gripping use of unmanned aerial vehicles (UAVs) has emerged as a new trending research direction [1, 2]. However, the grabbing scenes in the open world are very complex, which leads to the development of robotic grasping systems with advanced cognitive and adaptable grasping capabilities. To achieve high-level cognitive abilities, reinforcement learning embodiment is studied[3, 4]. In [3], Scalable Deep Reinforcement Learning is used to handle large amounts of off-policy image data for complex tasks like grasping. However, RL-based embodiment has posed challenges in terms of generalization capability, sample-effectiveness capability, and profound reasoning capability, especially in dynamic and uncertain real environments. Recently, Large multimodal models (LMMs), such as MiniGPT-4 [5] and LLaVA [6], have exhibited impressive performance in the domains of natural instruction-following and visual cognition. Therefore, LMMs are integrated with the physical world in the embodied agent. Apart from RL algorithms for specific tasks, LMMs-based agents have generalization capabilities [7, 8] though fine-tune methods, such as human demonstrations [9], vision-language cross-modal connector[10], ever-growing skill library [11] and so on. On-policy (RL) algorithms face challenges in terms of sample efficiency.

Accurately modeling soft robots remains a challenge due to their inherent nonlinear behavior and parameter variations. This paper presents a novel approach to modeling soft pneumatic actuators using a nonlinear parameter-varying framework. The research begins by introducing Ludwick's Law, providing a more accurate representation of the complex mechanical behavior exhibited by soft materials. Three key material properties, namely Young's modulus, tensile stress, and mixed viscosity, are utilized to estimate the parameter inside the nonlinear model using the least squares method. Subsequently, a nonlinear dynamic model for soft actuators is constructed by applying Ludwick's Law. To validate the accuracy and effectiveness of the proposed method, experimental validations are performed. We perform several experiments, demonstrating the model's capabilities in predicting the dynamical behavior of soft pneumatic actuators. In conclusion, this work contributes to the advancement of soft pneumatic actuator modeling that represents their nonlinear behavior.

In this paper, we present a methodology that uses an optical tactile sensor for efficient tactile exploration of embedded objects within soft materials. The methodology consists of an exploration phase, where a probabilistic estimate of the location of the embedded objects is built using a Bayesian approach. The exploration phase is then followed by a mapping phase which exploits the probabilistic map to reconstruct the underlying topography of the workspace by sampling in more detail regions where there is expected to be embedded objects. To demonstrate the effectiveness of the method, we tested our approach on an experimental setup that consists of a series of quartz beads located underneath a polyethylene foam that prevents direct observation of the configuration and requires the use of tactile exploration to recover the location of the beads. We show the performance of our methodology using ten different configurations of the beads where the proposed approach is able to approximate the underlying configuration. We benchmark our results against a random sampling policy.