Successful aerial manipulation largely depends on how effectively a controller can tackle the coupling dynamic forces between the aerial vehicle and the manipulator. However, this control problem has remained largely unsolved as the existing control approaches either require precise knowledge of the aerial vehicle/manipulator inertial couplings, or neglect the state-dependent uncertainties especially arising during the interaction phase. This work proposes an adaptive control solution to overcome this long standing control challenge without any a priori knowledge of the coupling dynamic terms. In addition, in contrast to the existing adaptive control solutions, the proposed control framework is modular, that is, it allows independent tuning of the adaptive gains for the vehicle position subdynamics, the vehicle attitude subdynamics, and the manipulator subdynamics. Stability of the closed loop under the proposed scheme is derived analytically, and real-time experiments validate the effectiveness of the proposed scheme over the state-of-the-art approaches.

Quadrupedal jumping has been intensively investigated in recent years. Still, realizing controlled jumping with soft landings remains an open challenge due to the complexity of the jump dynamics and the need to perform complex computations during the short time. This work tackles this challenge by leveraging trajectory optimization and behavior cloning. We generate an optimal jumping motion by utilizing dual-layered coarse-to-refine trajectory optimization. We combine this with a variable impedance control approach to achieve soft landing. Finally, we distill this computationally heavy jumping and landing policy into an efficient neural network via behavior cloning. Extensive simulation experiments demonstrate that, compared to classic model predictive control, the variable impedance control ensures compliance and reduces the stress on the motors during the landing phase. Furthermore, the neural network can reproduce jumping and landing behavior, achieving at least a 97.4% success rate. Hardware experiments confirm the findings, showcasing explosive jumping with soft landings and on-the-fly evaluation of the control actions.

Inspired by the dynamic coupling of moto-neurons and physical elasticity in animals, this work explores the possibility of generating locomotion gaits by utilizing physical oscillations in a soft snake by means of a low-level spiking neural mechanism. To achieve this goal, we introduce the Double Threshold Spiking neuron model with adjustable thresholds to generate varied output patterns. This neuron model can excite the natural dynamics of soft robotic snakes, and it enables distinct movements, such as turning or moving forward, by simply altering the neural thresholds. Finally, we demonstrate that our approach, termed SpikingSoft, naturally pairs and integrates with reinforcement learning. The high-level agent only needs to adjust the two thresholds to generate complex movement patterns, thus strongly simplifying the learning of reactive locomotion. Simulation results demonstrate that the proposed architecture significantly enhances the performance of the soft snake robot, enabling it to achieve target objectives with a 21.6% increase in success rate, a 29% reduction in time to reach the target, and smoother movements compared to the vanilla reinforcement learning controllers or Central Pattern Generator controller acting in torque space.

Motor imagery (MI), an important category in electroencephalogram (EEG) research, often intersects with scenarios demanding low energy consumption, such as portable medical devices and isolated environment operations. Traditional deep learning (DL) algorithms, despite their effectiveness, are characterized by significant computational demands accompanied by high energy usage. As an alternative, spiking neural networks (SNNs), inspired by the biological functions of the brain, emerge as a promising energy-efficient solution. However, SNNs typically exhibit lower accuracy than their counterpart convolutional neural networks (CNNs). Although attention mechanisms successfully increase network accuracy by focusing on relevant features, their integration in the SNN framework remains an open question. In this work, we combine the SNN and the attention mechanisms for the EEG classification, aiming to improve precision and reduce energy consumption. To this end, we first propose a noniterative leaky integrate-and-fire (NiLIF) neuron model, overcoming the gradient issues in traditional SNNs that use iterative LIF neurons for long time steps. Then, we introduce the sequence-based attention mechanisms to refine the feature map. We evaluated the proposed noniterative SNN with attention (NiSNN-A) model on two MI EEG datasets, OpenBMI and BCIC IV 2a. Experimental results demonstrate that: 1) our model outperforms other SNN models by achieving higher accuracy and 2) our model increases energy efficiency compared with the counterpart CNN models (i.e., by 2.13 times) while maintaining comparable accuracy.

Water-purifying materials are of vital importance for providing sanitary water, especially in epidemic and disaster areas and for wilderness survival. Here, we report a novel inorganic composite with a fast, reusable, and pleiotropic water purification ability. The composite of heterogeneous Ni-La₂O₃ nanofibers was developed using an easy, low-cost, and large-scale production process, i.e., an electrospinning technique. Because of the unique heterostructure formed by introducing nickel nanoparticles into the La₂O₃ host, the nanocomposite fibers can rapidly remove various detrimental micropollutants with absorption rate constants hundreds of times greater than those of inorganic materials and activated carbons. In addition, the nanocomposite can be recycled by a soft washing procedure at ambient temperature and demonstrates a much more stable performance than the state-of-the-art activated carbon materials. Furthermore, the nanofibers have an excellent germicidal effect against the high-risk human pathogen Escherichia coli. Our work introduces a promising inorganic absorbent suitable for purifying life-sustaining water supplies in emergency situations and alleviating worldwide water shortages.

Rare-earth zirconates (RE₂Zr₂O₇, RE = rare earth elements) are considered as promising thermal barrier coating (TBCs) materials. However, their thermal expansion coefficient (TEC) is relatively low comparing with that of either the state-of-the-art TBCs material Yttria-stabilized Zirconia (YSZ), or the metallic bond-coat. In present research, Cerium was introduced as the substitution of Zirconium to improve the TEC of rare-earth zirconates. A series of Yb₂(Zr_1−xCe_x)₂O₇ solid solutions were synthesized and investigated. Experimental results revealed that their TEC is remarkably increased with the Cerium substitution content. Meanwhile, their thermal conductivity shows no obvious increase compared with that of Yb₂Zr₂O₇due to the phonon scattering effect.

Pyrochlore-related compound Sm₂FeTaO₇ was synthesized and investigated. It is shown that the thermal conductivity values are approximately half of the Yttria-Stabilized Zirconia (YSZ) over the temperature range of 25–1000 °C. Especially, thermal conductivity at high temperature is close to that of minimum thermal conductivity, as predicted by theoretical investigation, signifying better heat insulation capabilities. Considering lower Young modulus, improved fracture toughness, and better high-temperature phase stability, Sm₂FeTaO₇ compound can be a promising material for thermal insulating applications, such as thermal barrier coatings.