The human somatosensory system is capable of extracting features with millimeter-scale spatial resolution and submillisecond temporal precision. Current technologies that can render tactile stimuli with such high definition are neither portable nor easily accessible. Here, we present a wearable electrotactile rendering system that elicits tactile stimuli with both high spatial resolution (76 dots/cm ² ) and rapid refresh rates (4 kHz), because of a previously unexplored current-steering super-resolution stimulation technique. For user safety, we present a high-frequency modulation method to reduce the stimulation voltage to as low as 13 V. The utility of our high spatiotemporal tactile rendering system is highlighted in applications such as braille display, virtual reality shopping, and digital virtual experiences. Furthermore, we integrate our setup with tactile sensors to transmit fine tactile features through thick gloves used by firefighters, allowing tiny objects to be localized based on tactile sensing alone.

Tactile sensations are mainly transmitted to each other by physical touch. Wireless touch perception could be a revolution for us to interact with the world. Here, we report a wireless self-sensing and haptic-reproducing electronic skin (e-skin) to realize noncontact touch communications. A flexible self-sensing actuator was developed to provide an integrated function in both tactile sensing and haptic feedback. When this e-skin was dynamically pressed, the actuator generated an induced voltage as tactile information. Via wireless communication, another e-skin could receive this tactile data and run a synchronized haptic reproduction. Thus, touch could be wirelessly conveyed in bidirections between two users as a touch intercom. Furthermore, this e-skin could be connected with various smart devices to form a touch internet of things where one-to-one and one-to-multiple touch delivery could be realized. This wireless touch presents huge potentials in remote touch video, medical care/assistance, education, and many other applications.

To accurately pour drinks into various containers is an essential skill for service robots. However, drink pouring is a dynamic process and difficult to model. Traditional deep imitation learning techniques for implementing autonomous robotic pouring have an inherent black-box effect and require a large amount of demonstration data for model training. To address these issues, an Explainable Hierarchical Imitation Learning (EHIL) method is proposed in this paper such that a robot can learn high-level general knowledge and execute low-level actions across multiple drink pouring scenarios. Moreover, with the EHIL method, a logical graph can be constructed for task execution, through which the decision-making process for action generation can be made explainable to users and the causes of failure can be traced out. Based on the logical graph, the framework is manipulable to achieve different targets while the adaptability to unseen scenarios can be achieved in an explainable manner. A series of experiments have been conducted to verify the effectiveness of the proposed method. Results indicate that EHIL outperforms the traditional behavior cloning method in terms of success rate, adaptability, manipulability, and explainability. Note to Practitioners—Pouring liquids is a common activity in people’s daily lives and all wet-lab industries. Drink pouring dynamic control is difficult to model, while the accurate perception of flow is challenging. To enable the robot to learn under unknown dynamics via observing the human demonstration, deep imitation learning can be used. To address the limitations of traditional deep neural networks, an Explainable Hierarchical Imitation Learning (EHIL) method is proposed in this paper. The proposed method enables the robot to learn a sequence of reasonable pouring phases for performing the task rather than simply execute the task via traditional behavior cloning. In this way, explainability and safety can be ensured. Manipulability can be achieved by reconstructing the logical graph. The target of this research is to obtain pouring dynamics via the learning method and realize the precise and quick pouring of drink from the source containers to various targeted containers with reliable performance, adaptability, manipulability, and explainability.

Teleoperation has widely contributed to many applications. Consequently, the design of intuitive and ergonomic control interfaces for teleoperation has become crucial. The rapid advancement of Mixed Reality (MR) has yielded tangible benefits in human-robot interaction. MR provides an immersive environment for interacting with robots, effectively reducing the mental and physical workload of operators during teleoperation. Additionally, the incorporation of haptic rendering, including kinaesthetic and tactile rendering, could further amplify the intuitiveness and efficiency of MR-based immersive teleoperation. In this study, we developed an immersive, bilateral teleoperation system, integrating Digital Twin-driven Mixed Reality (DTMR) manipulation with haptic rendering. This system comprises a commercial remote controller with a kinaesthetic rendering feature and a wearable cost-effective tactile rendering interface, called the Soft Pneumatic Tactile Array (SPTA). We carried out two user studies to assess the system's effectiveness, including a performance evaluation of key components within DTMR and a quantitative assessment of the newly developed SPTA. The results demonstrate an enhancement in both the human-robot interaction experience and teleoperation performance. For more project details, please view our website: https://sites.google.com/view/hbts-brl/home

In ecotoxicoproteomics, an accurate and reproducible extraction of proteins is a critical step for 2DE analysis and further protein identification using MS. The criteria for the assessment of protein extraction quality include protein yield, protein spots resolved in a 2DE gel, matched protein spots in replicate gels, reproducibility, and compatibility with MS. In this work, we evaluated three protein extraction systems, straightforward lysis buffer, trichloroacetic acid–acetone, and TRIzol reagent with some modifications, for the protein extraction from three animal species including mussel Mytilus galloprovincialis, flounder Paralichthys olivaceus, and polychaete Nereis diversicolor used in marine ecotoxicology. Our results indicated that these methods could extract significantly different protein profiles. The method using TRIzol reagent resulted in the most matched protein spots resolved in four replicate 2DE gels and highest reproducibilities for the gill of M. galloprovincialis and liver of P. olivaceus. However, a modified trichloroacetic acid–acetone solvent system was best for the whole soft tissue of N. diversicolor. This work provides the fundamental information of the extraction quality of protein extraction protocols from different marine animals, which may facilitate the selection of a suitable protein extraction protocol for ecotoxicoproteomics.