Designing a bipedal robot is a complex and challenging task, especially when dealing with a multitude of structural parameters. Traditional design methods often rely on human intuition and experience. However, such approaches are time-consuming, labor-intensive, lack theoretical guidance and hard to obtain optimal design results within vast design spaces, thus failing to full exploit the inherent performance potential of robots. In this context, this paper introduces the SERL (Structure Evolution Reinforcement Learning) algorithm, which combines reinforcement learning for locomotion tasks with evolution algorithms. The aim is to identify the optimal parameter combinations within a given multidimensional design space. Through the SERL algorithm, we successfully designed a bipedal robot named Wow Orin, where the optimal leg length are obtained through optimization based on body structure and motor torque. We have experimentally validated the effectiveness of the SERL algorithm, which is capable of optimizing the best structure within specified design space and task conditions. Additionally, to assess the performance gap between our designed robot and the current state-of-the-art robots, we compared Wow Orin with mainstream bipedal robots Cassie and Unitree H1. A series of experimental results demonstrate the Outstanding energy efficiency and performance of Wow Orin, further validating the feasibility of applying the SERL algorithm to practical design.

Traversing 3-D complex environments has always been a significant challenge for legged locomotion. Existing methods typically rely on external sensors such as vision and lidar to preemptively react to obstacles by acquiring environmental information. However, in scenarios like nighttime or dense forests, external sensors often fail to function properly, necessitating robots to rely on proprioceptive sensors to perceive diverse obstacles in the environment and respond promptly. This task is undeniably challenging. Our research finds that methods based on collision detection can enhance a robot’s perception of environmental obstacles. In this work, we propose an end-to-end learning-based quadruped robot motion controller that relies solely on proprioceptive sensing. This controller can accurately detect, localize, and agilely respond to collisions in unknown and complex 3D environments, thereby improving the robot’s traversability in complex environments. We demonstrate in both simulation and real-world experiments that our method enables quadruped robots to successfully traverse challenging obstacles in various complex environments. The videos and appendix can be found at Quad-Traverse-Go2.github.io

To realize the exquisite interaction and precise manipulation for the robot, in this article, we propose a multispectral multimodal visuotactile sensor named M$^{3}$Tac, which combines visible, near-infrared, and mid-infrared imaging technologies for the first time and can exceed the sensing ability of human skin in terms of resolution (719 pixels/cm$^{2}$), temperature sensing range (−20–130$^\text{o}$C), etc. The M$^{3}$Tac cannot only realize high-quality sensing of deformation, texture, force, stickiness, and temperature comparable to human skin but also can realize proximity sensing that is lacking for human skin. To achieve this, we not only design a multispectral imaging system with an elastic film whose light penetrability can be regulated by the brightness of the light, but also develop corresponding algorithms, including the pixel-level force sensing with finite element method (accuracy:$\pm$0.023N), the proximity perception (accuracy:$\pm$3.8 mm), the 3-D reconstruction (accuracy: 0.33 mm), the super-resolution temperature sensing (accuracy:$\pm 0.3^\text{o}$C), the multimodal fusion classification (accuracy: 98%), and the stickiness recognition (accuracy: 98%). Finally, we conduct experiments to verify the effectiveness and application potential of our research.

In our previous work, we proposed the extended PID (EPID) controller, which is a state-space extension of traditional PID control. Compared to PID control, EPID is more suitable for multi-input-multi-output (MIMO) and higher-order systems. In this paper, we further extend EPID to nonminimum phase systems and investigate its performance limitation. EPID uses feedback of all state tracking errors. But for nonminimum phase systems, the reference trajectories for the internal states are unknown (assuming we do not have the system model), making us decide to take out the internal states from the integral part to avoid an unbounded input, which results in a slightly different controller form. Besides, in previous study, we found an important property of EPID is that it can achieve accurate tracking/rejecting for time-varying references/disturbances by using a high integral gain. However, when applied to nonminimum phase systems, we found that the integral gain cannot be set too high, otherwise the closed-loop system will be unstable, which indicates an inherent performance limitation. To verify this, simulation results are provided by applying EPID to a hypersonic vehicle model and a cart pole system.

Manual oropharyngeal (OP) swab sampling is an intensive and risky task. In this article, a novel OP swab sampling device of low cost and high compliance is designed by combining the visuotactile sensor and the pneumatic actuator-based gripper. Here, a concave visuotactile sensor called CoTac is first proposed to address the problems of high cost and poor reliability of traditional multi-axis force sensors. Besides, by imitating the doctor's fingers, a soft pneumatic actuator with a rigid skeleton structure is designed, which is demonstrated to be reliable and safe via finite element modeling and experiments. Furthermore, we propose a sampling method that adopts a compliant control algorithm based on the adaptive virtual force to enhance the safety and compliance of the swab sampling process. The effectiveness of the device has been verified through sampling experiments as well as in vivo tests, indicating great application potential. The cost of the device is around 30 US dollars and the total weight of the functional part is less than 0.1 kg, allowing the device to be rapidly deployed on various robotic arms.

The grasping of transparent objects is challenging but of significance to robots. In this article, a visual–tactile fusion framework for transparent object grasping in complex backgrounds is proposed, which synergizes the advantages of vision and touch, and greatly improves the grasping efficiency of transparent objects. First, we propose a multiscene synthetic grasping dataset named SimTrans12 K together with a Gaussian-mask annotation method. Next, based on the TaTa gripper, we propose a grasping network named transparent object-grasping convolutional neural network for grasping position detection, which shows good performance in both synthetic and real scenes. Inspired by human grasping, a tactile calibration method and a visual–tactile fusion classification method are designed, which improve the grasping success rate by 36.7% compared with direct grasping and the classification accuracy by 39.1%. Furthermore, the tactile height sensing module and the tactile position exploration module are added to solve the problem of grasping transparent objects in irregular and visually undetectable scenes. The experimental results demonstrate the validity of the framework.

Large-area and high-precision tactile sensing information can not only improve the stability of robot grasping but also compensate for the lack of visual information in specific environments such as turbid underwater, dimness, and smoke. In this paper, we devise a universal jamming gripper with high-quality tactile sensing capability. The gripper adopts the particle jamming mechanism for grasping, and simultaneously uses a built-in camera to detect the deformation of its surface to obtain tactile information. To make the inside of the gripper transparent, glass beads and liquid with the same refractive index are applied as the internal filling. Besides, special treatments are taken to improve the tactile perception resolution of the gripper. The design perfectly merges visual-based tactile sensing into the traditional universal jamming gripper without changing its original gripping performance, making it possible for simultaneous grasping and sensing. To verify the tactile perception and grasping ability of the gripper in specific environments, we design two underwater experiments for grasping and pipe leak detection based on tactile information. Both have achieved a success rate not less than 95%, which demonstrates the effectiveness of the proposed gripper for manipulation in low visibility environments.

The attitude control of an underactuated reusable launch vehicle (RLV) in the reentry phase involving nonminimum phase problem and control input constraints is investigated in this article. To address the nonminimum phase problem, an approach combining output redefinition and robust backstepping is proposed, where a synthetic output is constructed using the combination of the original output and the internal states to obtain stable zero dynamics, and then robust backstepping is performed on the new output. Besides, the ideal internal dynamics are obtained by using optimal bounded inversion, which are incorporated into the controller as the reference trajectories for the internal states to improve the output tracking accuracy. To cope with the control input constraints, a simple and useful anti-windup strategy is proposed by using feedback error clipping, which is shown to be very effective in mitigating control input saturation. Numerical simulations are given to validate the effectiveness of the proposed method.

Although computer vision has the advantages of long detection distance and large amount of information, it also has certain limitations for complex scenes such as dimness, reflections, and smoke. In order to solve the problem of robot grasping in these scenes, we designed a novel gripper that can search, identify and grasp objects based on tactile information. The gripper can effectively grasp the objects in real life, and can sense the shape and posture of the objects through the touch. We proposed a lifting finger structure that allows the gripper to switch between sensing and grasping modes. We applied visual-tactile detection methods to obtain tactile information and propose a feature extraction algorithm based on U-net. We designed a method of grasping the center of mass of the object contour, and the success rate of the grasping can reach 85%. In addition, we also designed experiments to show the feasibility of object searching and grasping by tactile information when visual information is not available.

Symmetry in running was observed by Marc Raibert and was applied to simplify the control of dynamic legged systems. In this paper, we show that symmetry also exists in biped walking and investigate it using two simplified 2D models, that are, the inverted pendulum (IP) model and the linear inverted pendulum (LIP) model, both leading to similar conclusions. To characterize the symmetry in biped walking, the concept of acceleration factor is proposed. Symmetry occurs when the acceleration factor is zero, which results in an unchanged mid-stance velocity. And an important property of symmetry is that the n-step reachable region and the n-step controllable region are exactly the same. This means that if we can achieve speed B from A in n steps, then we can also achieve speed A from B in n steps. Symmetry in walking helps us to better understand human walking and also provides an intuitive way to control robotic walking. As an example, we propose a feedforward controller and a feedback controller, respectively, which can regulate the walking speed very effectively. This work provides us some new insights to view biped walking.

In this paper, an extremely simple yet super effective universal tracking controller (UTC) is developed based on integral chain system, which can achieve accurate tracking for continuous signals as well as superior rejection for disturbances regardless of system models. We discovered two natural formulations for UTC: one is the proportional-integral tracking controller (PITC), which includes a proportional and an integral part of states errors; the other is the adaptive feedforward tracking controller (AFTC), which consists of a feedback part of state errors and a feedforward part which is obtained adaptively by using the previous sampled input. When the integral gain is high, it is found that PITC and AFTC are approximately equivalent with the integral gain be proportional to the sampling rate. Thus PITC and AFTC together form a unified framework of UTC, where PI and PID are only particular cases of first-order and second-order PITC, respectively.

Accurate tracking control of nonminimum phase systems relies on the calculation of the ideal internal dynamics (IID). Traditional IID calculation methods fail when applied to nonminimum phase nonhyperbolic systems (systems with nonhyperbolic zero dynamics). Recently, we propose the optimal bounded inversion method for IID calculation, which obtains IID by solving a trajectory optimization problem. In this paper, we extend our previous result and show that optimal bounded inversion can also deal with nonminimum phase nonhyperbolic systems. More than that, it is also possible to achieve different control goals by setting different cost functions. Particularly, three cases are investigated in this paper. The first uses minimal initial value deviation as the cost function, resulting in "T-IID" which can achieve accurate output tracking. The second applies minimal terminal value as the cost function, resulting in "S-IID" which leads to a final rest for the system. The last combines "T-IID" and "S-IID" to achieve a compound goal. The effectiveness is verified through Matlab simulations of a two-cart inverted-pendulum system.

The precision output tracking problem of multi-input-multi-output (MIMO) uncertain nonlinear nonminimum phase systems is investigated in this paper. The challenge lies in the difficulty in solving the ideal internal dynamics (IID), which is a bounded solution for the uncertain zero dynamics. First, the experience replay technique is applied to identify the uncertain parameters in the zero dynamics. It uses the recorded past data concurrently with current data which can greatly speed up the identification convergence. With the identified parameters, a novel approach called optimal bounded inversion is proposed to obtain the IID by solving a trajectory optimization problem using GPOPS-II. The boundedness of the IID is guaranteed by setting state constraints and the feasibility is achieved by minimizing the initial condition mismatch. Moreover, a piecewise IID updating scheme is adopted to reduce the computational burden. The benchmark nonminimum phase system, a vertical take-off and landing (VTOL) aircraft is used to validate the effectiveness of the proposed method.

This paper proposes a multi-task control strategy for a quadruped robot named THU-QUAD II. The mechanical design of the robot ensures a wide range of motion for all joints, which allows it to stand and walk like a mammal as well as sprawl to the ground and crawl like a reptile. Five basic leg configurations are defined for the robot, including four mammal-type configurations with bidirectional knees and one sprawling-type configuration. A multi-task control framework is developed by combining configuration selection and gait planning. According to the locomotion environments, the robot can nimbly switch between different configurations, which gives it more flexibility when facing different tasks. For the mammal-type configuration, a parametric climbing gait is designed to traverse structural terrain. For the sprawling-type configuration, a crawling gait is designed to achieve robust locomotion on uneven terrain. Simulations and experiments show that the robot is capable to move on multiple challenging terrains, including doorsills, stairs, slopes, sand and stones. This paper demonstrates that even some challenging locomotion tasks can be achieved in a rather simple way without using complicated control algorithms, which suggests us to rethink about the leg configurations in designing quadruped robots.

Traversing at a high speed while maintaining stability is important for the application of quadruped robots. Prior works mainly concentrated on optimizing the stability margin of quadruped robots when walking through a variety of terrains. However, the problem of improving quadruped robots' walking velocity with static gait is less concerned in their works. In this paper, the static gait planning problem is considered under the assumption that a set of irregular footholds on the rough terrain is given, and two approaches are proposed to improve the walking speed. The first one is a distance optimization algorithm, which can minimize the moving distance of the center of gravity (COG) in the stance phases based on the stability and the kinematic constraint. The other is a velocity optimization algorithm, which enables the body and the feet to move at the highest velocity with the joint angular velocity limit. The joint application of these two optimization algorithms significantly improves the walking speed of the quadruped robot. Simulation results in V-REP are presented to demonstrate the effectiveness of the proposed approaches in improving the walking speed. Compared with the traditional gait planning techniques, one that moves the robot with the optimal stability margin, and the other that moves the robot without optimizing the velocity, our algorithms increase the average walking velocity by 81.6% and 32.8%, respectively.