Using soft pneumatic actuators is a feasible solution in the dynamic unstructed environment, thanks to their light weight and inherent compliance. It is commonly agreed that the passive compliance and adaptivity of soft robots make them much safer. However, under some special circumstances where stringent safety is requested (e.g., surgical operation), the passive compliance is far from meeting the demands. Therefore, active compliance control is still required to guarantee their operating safety. In this paper, a novel soft actuator is presented, whose stiffness is tunable in multiple ways, and more than a 10-fold stiffness enhancement is achievable, making it able to carry heavy loads while maintaining excellent dexterity and compliance. Meanwhile, we first proposed an active safe control strategy based on inertial measurement units (IMUs). Through calibrating the load weight (i.e., weight of the end tools) and measuring vibration of soft structures, the collision can be detected in real-time and the corresponding safe reaction is then adopted. We have validated its effectiveness on human body, the strategy still works at the speed of 200 mm/s with sharp tools. Furthermore, our control strategy can be applied to other soft or continuum robots, making them safer and more practical in actual applications.

In this paper, we introduce an AI-mediated framework that can provide intelligent feedback to augment human cognition. Specifically, we leverage deep reinforcement learning (DRL) to provide adaptive time pressure feedback to improve user performance in a math arithmetic task. Time pressure feedback could either improve or deteriorate user performance by regulating user attention and anxiety. Adaptive time pressure feedback controlled by a DRL policy according to users’ real-time performance could potentially solve this trade-off problem. However, the DRL training and hyperparameter tuning may require large amounts of data and iterative user studies. Therefore, we propose a dual-DRL framework that trains a regulation DRL agent to regulate user performance by interacting with another simulation DRL agent that mimics user cognition behaviors from an existing dataset. Our user study demonstrates the feasibility and effectiveness of the dual-DRL framework in augmenting user performance, in comparison to the baseline group.

Accurate 3D reconstruction of objects has recently made great breakthroughs, thanks to the development of computer vision. Whereas precise 3D information can merely reflect an object’s geometry information, which is limited in many applications. Exploration of more information is needed to fulfill diverse tasks. As an important property of an object, hardness can provide more comprehensive information which can be widely used in various tasks like grasping and manipulation. In this paper, a framework combining active perception and motion planning algorithm is proposed to get both hardness and geometry information of an object which also ensures working efficiency. In this framework, a stylus mounted on a robotic arm explores hardness and geometry information on the surface of the object actively and a depth camera is used to capture raw 3D shape information. A novel motion planning algorithm is proposed to keep the exploration operative and time-saving. Experimental results show that our framework has good performance and can explore global hardness and geometry information efficiently.

Increasing individuals' awareness of their own body signals can lead to improved interoception, enabling the brain to estimate current body states more accurately and in a timely manner. However, certain body signals, such as eye movements, often go unnoticed by individuals themselves. This study aimed to test the hypothesis that providing eye-movement-correlated tactile feedback on the body enhances individuals' awareness of their attentive states, subsequently improving attention. Our results demonstrate the effectiveness of such feedback in redirecting and enhancing attention, particularly in the presence of distractions during long-duration tasks. Additionally, we observed that people's gaze behaviors changed in response to the tactile feedback, suggesting an increased self-awareness of current eye movements and attentive states. Ultimately, these changes in gaze behaviors contribute to the modulation of attentive states. Our findings highlight the potential of eye-movement-correlated bodily tactile feedback to increase individuals' self-awareness of their eye movements and attentive states. By providing real-time feedback through tactile stimuli, we can actively engage individuals in regulating their attention and enhancing their overall performance.