In nature, animals with exceptional locomotion abilities, such as cougars, often exhibit asymmetry between their forelimbs and hindlimbs. This observation inspires our investigation into whether optimizing leg length in legged robots can impart similar locomotion capabilities. In this article, we propose a method that cooptimizes mechanical structure and control policies to enhance the locomotion performance of legged robots. Specifically, we introduce a pretraining-finetuning framework that not only ensures optimal control strategies for each mechanical configuration but also enhances time efficiency. Furthermore, we develop an innovative training approach for our pretraining network that integrates spatial domain randomization with discount regularization techniques, significantly improving the network’s generalizability. The effectiveness of the proposed method is validated through two quadrupedal locomotion tasks and two parkour-style tasks. Experimental results demonstrate that the pretraining-finetuning framework markedly enhances overall codesign performance with reduced time expenditure. Additionally, experimental validation is performed, confirming that the cooptimized morphology and control strategy effectively mitigate the robot’s energy expenditure during designated tasks.

Legged robots hold promise for traversing challenging terrains, which is crucial for tasks, such as emergency response and inspections. Although the locomotion capabilities of legged robots have greatly improved, navigating unknown and uneven environments in real-time applications remains a key challenge. This is because existing controllers typically have a short prediction horizon and often require manual remote control. In this article, we propose a fast, foothold-aware path planner that enables rapid planning while simultaneously taking into account the feasibility of footholds along the path, and an online foothold and trajectory planner that plans appropriate footholds and collision-free whole-body trajectories in real-time. This layered approach does not require the long offline computation time, allowing for both real-time and long-horizon planning. We then integrate these planners with existing controllers and the perception module to achieve fully autonomous navigation of legged robots over uneven and unknown terrains. Extensive simulations and real-world experiments validate the effectiveness of our approach, demonstrating enhanced performance in navigating highly challenging terrain scenarios.

Small-scale legged robots have found widespread utilization in various industrial and biomedical applications due to their compact size and superior locomotion capabilities. Reducing the number of actuators is often desirable to decrease the robot's size and weight, which comes at the expense of the robot's workspace. Our study proposes a method to enhance the mobility of small-scale legged robots with limited degrees of actuators (DoAs) by co-optimizing both morphology parameters and control policy. The co-optimization is formulated as a bi-level optimization problem, where the control policy is designed using deep reinforcement learning algorithms and central pattern generators (CPGs) at the lower level. The inclusion of CPGs significantly speeds up training and enables the application of simulation results in real-world scenarios. At the upper level, morphology optimization is achieved through Bayesian optimization based on dual-networks. This approach eliminates the need to train a policy for each morphology candidate from scratch, leveraging previous experience to enhance efficiency. Through simulation and physical experiments, the effectiveness of our proposed approach is demonstrated, showcasing its ability to discover optimal morphology and gait for small-scale legged robots with limited DoAs. These findings have potential long-term impacts on small-scale legged robot design and locomotion control.

Significant progress has been made in enhancing the motion capabilities of quadruped robots in unstructured environments due to advancements in hardware and control algorithms. However, limited research has been conducted on the fault-tolerant control of quadruped robots, which is crucial for their operation in remote or extreme environments like disaster sites. In this paper, we primarily focus on fault-tolerant strategies for common joint-stuck situations. By leveraging the static stability of quadruped robots, it becomes possible to adjust their control policies and enable them to continue following predetermined trajectories. We introduce a contextual meta-reinforcement learning (Meta-RL) method to design fault-tolerant policies. This method infers task-related latent vectors from the context to assist in training the policy network, ensuring both conciseness and optimality in various situations. Additionally, to expedite algorithm training, we propose a reference action generator (RAG). To validate the proposed algorithm, extensive simulations and physical experiments are conducted. The results demonstrate that our method allows the robot to maintain its trajectory even when faced with motor locking. Furthermore, our method outperforms all baseline algorithms, highlighting its superiority in terms of fault tolerance. Note to Practitioners —The motivation of this article is to provide fault-tolerant policies for quadruped robots, specifically referring to the policies for joint-stuck situations. Previous fault-tolerant strategies either require individually designing control strategies for each joint stuck task, which brings a significant workload to designers, or adopting a unified strategy that cannot provide the optimal strategy for each task. In this article, we utilize the Meta-RL method to handle the joint stuck issue in robots for the first time. By combining the context encoder and RAG, we can provide more suitable policies for various motor-stuck tasks. Both the simulation and physical experiments validate the effectiveness and applicability of this method.

In nature, animals with exceptional locomotion abilities, such as cougars, often possess asymmetric fore and hind legs. This observation inspired us: could optimizing the leg length of quadruped robots endow them with similar locomotive capabilities? In this paper, we propose an approach that co-optimizes the mechanical structure and control policy to boost the locomotive prowess of quadruped robots. Specifically, we introduce a novel pretraining-finetuning framework, which not only guarantees optimal control strategies for each mechanical candidate but also ensures time efficiency. Additionally, we have devised an innovative training method for our pretraining network, integrating spatial domain randomization with regularization methods, markedly improving the network's generalizability. Our experimental results indicate that the proposed pretraining-finetuning framework significantly enhances the overall co-design performance with less time consumption. Moreover, the co-design strategy substantially exceeds the conventional method of independently optimizing control strategies, further improving the robot's locomotive performance and providing an innovative approach to enhancing the extreme parkour capabilities of quadruped robots.

Legged robot proved their capability to cross complex terrain in recent research, yet the autonomy of robots on discrete terrain still needs to be enhanced since it requires a full stack framework. This paper introduces a real-time motion and foothold planning framework tailored for legged robots navigating uneven terrains, such as stepping stones. Our approach addresses the critical challenges of determining feasible global paths and local footholds to enhance autonomous mobility across complex landscapes. By using a sampling-based global path planner integrated with terrain segmentation and the robot's kinematic model, our framework swiftly generates viable navigation paths. Concurrently, it utilizes a Mixed Integer Programming (MIP) methodology for real-time foothold optimization, ensuring the robot's stability and safety through dynamic terrain interaction. Finally, an execution layer including Model Predictive Control (MPC) and Whole-Body Control (WBC) generates the robots' motion. Simulation and real-world experiments demonstrate that our framework improves legged robots' adaptability on discrete terrains.

记者问:您能给我们简单的介绍一下脂肪肝的危害、高危因素以及治疗方式吗? 孙超博士:非酒精性脂肪性肝病(NAFLD,以下简称脂肪肝)是一种多系统受累的疾病,不仅会引起肝纤维化、肝硬化,甚至肝癌和肝功能衰竭,也与糖尿病、心脑血管疾病、慢性肾病以及肝外恶性肿瘤的高发密切相关.高热量的饮食结构、不健康的饮食习惯、久坐少动的生活方式、长期缺乏体育锻炼是脂肪肝的重要危险因素.

人物档案 徐京杭:医学博士,副主任医师,副教授,硕士生导师,美国国立卫生研究院/国立癌症研究所访问学者. 现任北京大学医学部传染病学系成员,中华医学会肝病学分会工作秘书、青年学组副组长、肝纤维化肝硬化门静脉高压学组副组长、肝病相关感染协作组成员,中华医学会感染病学分会青年学组成员,北京医学会肝病学分会委员,北京中西医结合学会传染病专业委员会副主任委员,北京医学会感染病学分会青年学组成员.

记者问:什么是原发性胆汁性胆管炎(PBC)? 陈燕飞博士:原发性胆汁性胆管炎(之前称为原发性胆汁性肝硬化)是一种自身免疫性肝脏疾病,主要发病人群为中老年女性,近年来发现男性患者也有增多,PBC典型的肝功能异常为碱性磷酸酶(ALP)、谷氨酰转肽酶(γ-GT/GGT)升高,其中ALP特异性更高,多高于正常高限2倍以上,见于95％以上的PBC患者.

记者问:在定期进行全身的体检中有相当一部分人做B超检查会发现有肝囊肿,请教您一下:肝囊肿是啥? 陈慧婷主任:门诊经常会碰到一些患者拿着提示肝囊肿的B超结果到门诊咨询,有些患者会被吓着,以为肝脏长肿瘤了,问该怎么办.肝囊肿是一种常见病、多发病,它是在肝脏内生长的囊性占位性病变,像充满液体的水囊,长在肝脏内部压迫周围,或从肝表面向外突出.

记者问:肝硬化和糖尿病是两种常见的慢性疾病,您在肝硬化领域做了多年研究,遇到肝硬化合并糖尿病患者很多,能不能请您给我们介绍一下肝硬化患者糖尿病患病率情况? 张晶教授:肝硬化,特别是存在肝功能损伤时,会影响糖代谢,甚至出现糖耐量减低和以餐后血糖升高为主的糖尿病.利用持续葡萄糖监测技术发现几乎所有的肝硬化患者都有糖耐量减低或糖尿病.

Motor locking is a common issue in quadruped robots that can have serious consequences if the robot continues executing its original commands. However, the static stability of the quadruped allows for the flexibility to adjust the robot's control policy so that it can maintain movement along a predetermined trajectory. In this paper, we introduce a residual meta reinforcement learning method comprising a trajectory generator and a meta-reinforcement learning corrector. The trajectory generator generates a reference joint position, while the corrector utilizes contextual reasoning to determine the appropriate action in the event of a motor locking. This action is employed to rectify the reference joint position, resulting in a fault-tolerant control strategy for the robot. We conducted comprehensive simulation experiments to validate our proposed algorithm, which demonstrates that the robot can still follow the predefined trajectory, even in the presence of a motor locking. Moreover, our proposed approach outperforms all baseline algorithms.

记者问:什么是肝静脉压力梯度测定? 陈世耀教授:门静脉高压是影响肝硬化患者临床预后的重要因素,其严重程度决定了肝硬化并发症的发生和发展,包括食管胃静脉曲张破裂出血、腹腔积液、肝肾综合征等;而临床上直接测量门静脉压力创伤大、风险高,且腹内压力改变等因素会对结果造成干扰,因此,通过肝静脉压力梯度(HVPG)测定间接评估门静脉压力,通过测定肝静脉楔压(WHVP)和肝静脉自由压(FHVP)之间的差值,反映了门静脉和腹内腔静脉之间的压力差,HVPG消除了腹腔内压力对测量结果的影响,具有安全性、可行性和可重复性等优点.

记者问:什么是乙肝妈妈? 陈琳教授:"乙肝妈妈"指的是乙肝病毒表面抗原(HBsAg)阳性的孕产妇.也就是患有乙肝或者体内携带乙肝病毒的孕产妇. 记者问:乙肝育龄期妇女应该如何备孕? 陈琳教授:母婴传播是HBV感染的重要途径,同时婴幼儿时期HBV感染易形成慢性感染状态,很多乙肝女性都担忧自己能不能孕育健康宝宝.

With the increasing computing power, using data-driven approaches to co-design a robot's morphology and controller has become a promising way. However, most existing data-driven methods require training the controller for each morphology to calculate fitness, which is time-consuming. In contrast, the dual-network framework utilizes data collected by individual networks under a specific morphology to train a population network that provides a surrogate function for morphology optimization. This approach replaces the traditional evaluation of a diverse set of candidates, thereby speeding up the training. Despite considerable results, the online training of both networks impedes their performance. To address this issue, we propose a concurrent network framework that combines online and offline reinforcement learning (RL) methods. By leveraging the behavior cloning term in a flexible manner, we achieve an effective combination of both networks. We conducted multiple sets of comparative experiments in the simulator and found that the proposed method effectively addresses issues present in the dual-network framework, leading to overall algorithmic performance improvement. Furthermore, we validated the algorithm on a real robot, demonstrating its feasibility in a practical application.

Self-assessment rules play an essential role in safe and effective real-world robotic applications, which verify the feasibility of the selected action before actual execution. But how to utilize the self-assessment results to re-choose actions remains a challenge. Previous methods eliminate the selected action evaluated as failed by the self-assessment rules, and re-choose one with the next-highest affordance (i.e. process-of-elimination strategy [1]), which ignores the dependency between the self-assessment results and the remaining untried actions. However, this dependency is important since the previous failures might help trim the remaining over-estimated actions. In this paper, we set to investigate this dependency by learning a failure-aware policy. We propose two architectures for the failure-aware policy by representing the self-assessment results of previous failures as the variable state, and leveraging recurrent neural networks to implicitly memorize the previous failures. Experiments conducted on three tasks demonstrate that our method can achieve better performances with higher task success rates by less trials. Moreover, when the actions are correlated, learning a failure-aware policy can achieve better performance than the process-of-elimination strategy.

众所周知乙型肝炎病毒感染的途径主要有三种:母婴垂直传播、体液血液传播和性传播,其中母婴传播是导致慢乙肝的最主要途径.由于感染了乙肝后并没有任何症状,很多孕妇都不知道自己已经感染了乙肝病毒,加之以前没有很好的母婴阻断措施,因此如果母亲是乙肝病毒携带或慢乙肝患者,其子女都会被传染,成为乙肝病毒携带者,也就是我们通常说的"乙肝家庭聚集"现象.

In the process of operating, robots will inevitably encounter damage due to external or internal factors, such as motors blockage. For the legged robot, when the motors of joints are failing, if other motors still act according to the original instructions, it will cause the robot to deviate from the predetermined trajectory, which is unacceptable for legged robots. Inspired by the fact that the model trained by supervised learning on the training set can be generalized to the testing set, our goal is to obtain a dynamic model that can be generalized to all kinds of motor damage situations. It can predict what state will be reached in the next step when an action is applied in the current state. With this dynamics model, we use the Monte Carlo particles to optimize the feasible actions in a model predictive control (MPC) fashion and achieve the expected goal (such as making the robot walk in a straight line). The comparison experiment adopt two meta-learning model and vanilla dynamics model approaches, the results show that the proposed method is superior to the three baselines, which proves the effectiveness of the proposed method.

记者问:目前很多患者处于慢性HBV携带状态和非活动性HBsAg携带状态暂未治疗的患者,这些患者并未出现相关症状,那么他们是否也和其他患者一样需要到医院进行定期的检查吗? 辛永宁教授:乙肝感染的自然史取决于病毒和机体相互作用,不是说病情稳定的状态会一成不变,也就是说并不是只有症状出现了才需要开始进行检查随访.通常对这部分患者,我们建议每6-12个月进行血常规、肝脏生化、HBV DNA、甲胎蛋白、腹部超声和无创肝纤维化等检查,必要时进行肝活检.慢性乙肝患者进行定期复查是为了监测乙肝病情的动态变化,对慢性乙肝患者的病情变化做到早发现、早治疗.若符合抗病毒治疗指征,及时启动规范治疗,可以实现更好的预防和控制病情的进展.

记者问:常见的威胁肝脏健康的因素有哪些? 陈琳教授:首先是各种肝炎病毒感染,其次某些西药、中药、保健品的应用等可能会导致药物性肝损伤.尤其是原本有脂肪肝、乙肝等基础肝病的患者,如果再滥用药物,肝脏受损程度会加重.因此建议包括患慢性肝病在内的所有人群,在服用药物之前,应详细阅读药物说明书,严格遵照医嘱用药,不提倡自行服药或多种药物同时服用.