CEACAM1 dampens antitumor immunity by down-regulating NKG2D ligand expression on tumor cells

By retaining NKG2D ligands within tumor cells, carcinoembryonic antigen–related cell adhesion molecule 1 (CEACAM1) facilitates tumor cell escape from NK cell–mediated cytolysis in vitro and in vivo

Historical Developments of BHR Humanoid Robots

Humanoid robots can achieve increasingly complex functions and adapt to more complex environments. To boost the development of humanoid robot technology, a team at Beijing Institute of Technology initiated the research on humanoid robots from 2000. Their research primarily focuses on stable walking, whole-body complex motion, human-robot interaction, and multimodal motion of humanoid robots. Thus far, the team has developed 6 generations of humanoid robots. The latest humanoid robot, BHR-6P, can achieve multi-mode motions (for example, walk, jump, fall protection, crawl and roll), which will significantly improve the ability of robot to adapt to the environment. This paper presented the historical evolution of BHR humanoid robots and outlined their functions and features

The Short Isoform of the CEACAM1 Receptor in Intestinal T Cells Regulates Mucosal Immunity and Homeostasis via Tfh Cell Induction

Carcinoembryonic antigen cell adhesion molecule like I (CEACAM1) is expressed on activated T cells and signals through either a long (L) cytoplasmic tail containing immune receptor tyrosine based inhibitory motifs, which provide inhibitory function, or a short (S) cytoplasmic tail with an unknown role. Previous studies on peripheral T cells show that CEACAM1-L isoforms predominate with little to no detectable CEACAM1-S isoforms in mouse and human. We show here that this was not the case in tissue resident T cells of intestines and gut associated lymphoid tissues which demonstrated predominant expression of CEACAM1-S isoforms relative to CEACAM1-L isoforms in human and mouse. This tissue resident predominance of CEACAM1-S expression was determined by the intestinal environment where it served a stimulatory function leading to the regulation of T cell subsets associated with generation of secretory IgA immunity, the regulation of mucosal commensalism, and defense of the barrier against enteropathogens

Ball Tracking and Trajectory Prediction for Table-Tennis Robots

Sports robots have become a popular research topic in recent years. For table-tennis robots, ball tracking and trajectory prediction are the most important technologies. Several methods were developed in previous research efforts, and they can be divided into two categories: physical models and machine learning. The former use algorithms that consider gravity, air resistance, the Magnus effect, and elastic collision. However, estimating these external forces require high sampling frequencies that can only be achieved with high-efficiency imaging equipment. This study thus employed machine learning to learn the flight trajectories of ping-pong balls, which consist of two parabolic trajectories: one beginning at the serving point and ending at the landing point on the table, and the other beginning at the landing point and ending at the striking point of the robot. We established two artificial neural networks to learn these two trajectories. We conducted a simulation experiment using 200 real-world trajectories as training data. The mean errors of the proposed dual-network method and a single-network model were 39.6 mm and 42.9 mm, respectively. The results indicate that the prediction performance of the proposed dual-network method is better than that of the single-network approach. We also used the physical model to generate 330 trajectories for training and the simulation test results show that the trained model achieved a success rate of 97% out of 30 attempts, which was higher than the success rate of 70% obtained by the physical model. A physical experiment presented a mean error and standard deviation of 36.6 mm and 18.8 mm, respectively. The results also show that even without the time stamps, the proposed method maintains its prediction performance with the additional advantages of 15% fewer parameters in the overall network and 54% shorter training time

Design of a Redundant Manipulator for Playing Table Tennis towards Human-Like Stroke Patterns

This study investigates the design of a 7-DOF humanoid manipulator capable of playing table tennis with human-like stroke patterns. The manipulator system includes a redundant arm, real-time stereo vision system, and a distributed motion control system. First, the size, weight, workspace, and motion capability of the designed arm are similar to those of a human's arm. The forward and inverse kinematics, and the Jacobian matrix of the redundant manipulator are formulated. Next, a distributed motion control system is designed. The ball trajectory prediction method is proposed. Then, a human-inspired optimization method based on Jacobian pseudoinverse and the comfort of the arm posture for stroke pattern trajectory is proposed to achieve human-like stroke patterns and decrease the counterforce exerted on the manipulator. Finally, the validity of the proposed system and methods is demonstrated via human-like stroke pattern experiments

Design of a Felid-like Humanoid Foot for Stability Enhancement

The foot is an important part of humanoid robot locomotion that can help with shock absorption while making contact with the ground. The mechanism of the foot directly affects walking stability. A novel foot mechanism inspired by the toes of felids is proposed. The foot has four bionic modules with soft pads and sharp claws installed at the four corners of a flat foot. This foot can reduce the impact experienced during foot landing and increase the time that the foot is in contact with the ground, which can improve the adaptability of the robot to different ground surface conditions with different levels of stiffness. The main structure of the bionic module is a four-bar linkage consisting of a slide way and a spring. Furthermore, the length of the four-bar linkage and the posture of the claw during insertion into soft ground are optimized to improve the stability and buffering performance. The validity of the proposed foot mechanism has been proved in simulations

Development Strategy for Air–Ground Collaborative Multi-Modal Intelligent Robot System

An air–ground collaborative multi-modal intelligent robot system can adapt to air and ground environments, has multimodal characteristic and advanced intelligence, and is able to complete complex tasks. The system has broad application prospects in various fields of society and is a new driving force for global technological, social, and economic development. Considering the major demand for developing the air–ground collaborative multi-modal intelligent robot system in China, this study comprehensively analyzes the development status of the system in China and abroad and the existing problems in China. China currently lags behind the international advanced level in terms of the air–ground collaborative intelligent robot system development; however, it still has the opportunity to achieve the advanced level as the system is still developing in its infancy in countries worldwide. The system involves theories, key technologies, core components and units, platforms, system equipment, and system applications and aims to build related technical system, core component industry system, intelligent robot equipment system, and social applications. Moreover, we propose the development layout, roadmap, and suggestions for the development of the system. Research shows that the air–ground collaborative multi-modal intelligent robot system can be integrated into the future smart society in all aspects and be applied to fields such as smart medical, education, housing, transportation, and manufacturing, to contribute to the national economy and people’s livelihood

Impact motion control of humanoid robot BHR-5 based on the energy integral method

To replace human beings for a task conducted in a realistic environment, the ability to perform impact motions such as running, jumping, and carrying is required for a humanoid robot. At present, it is quite difficult for the humanoid robot to achieve the motion performance of human beings. To address this issue, this article proposes to use a novel impact motion control method for a humanoid robot called BHR-5 using energy integral method. First, we have designed a high power density joint controller to drive each brushless motor to actuate each robot joint. The joint controller can generate sufficient power to actuate the robot joint with an instantaneous overload when limiting the input power in a unit interval. Second, the control system can generate trajectories to realize impact motion by integrating the energy of the whole body. Finally, we have conducted performance test on BHR-5 to verify the control method using our motor drivers and trajectory generation method. Analysis of the experimental results confirmed the effectiveness of the proposed control method for performing impact motions

Turning Gait Planning Method for Humanoid Robots

The most important feature of this paper is to transform the complex motion of robot turning into a simple translational motion, thus simplifying the dynamic model. Compared with the method that generates a center of mass (COM) trajectory directly by the inverted pendulum model, this method is more precise. The non-inertial reference is introduced in the turning walk. This method can translate the turning walk into a straight-line walk when the inertial forces act on the robot. The dynamics of the robot model, called linear inverted pendulum (LIP), are changed and improved dynamics are derived to make them apply to the turning walk model. Then, we expend the new LIP model and control the zero moment point (ZMP) to guarantee the stability of the unstable parts of this model in order to generate a stable COM trajectory. We present simulation results for the improved LIP dynamics and verify the stability of the robot turning