Wheeled-legged robots combine the efficiency of wheels with the versatility of legs, but face significant energy optimization challenges when navigating diverse environments. In this work, we present a hierarchical control framework that integrates predictive power modeling with residual reinforcement learning to optimize omnidirectional locomotion efficiency for wheeled quadrupedal robots. Our approach employs a novel power prediction network that forecasts energy consumption across different gait patterns over a 1-second horizon, enabling intelligent selection of the most energy-efficient nominal gait. A reinforcement learning policy then generates residual adjustments to this nominal gait, fine-tuning the robot’s actions to balance energy efficiency with performance objectives. Comparative analysis shows our method reduces energy consumption by up to 35% compared to fixed-gait approaches while maintaining comparable velocity tracking performance. We validate our framework through extensive simulations and real-world experiments on a modified Unitree Go1 platform, demonstrating robust performance even under external disturbances. Videos and implementation details are available at https://sites.google.com/view/switching-wpg.

Learning an effective policy to control high-dimensional, overactuated systems is a significant challenge for deep reinforcement learning algorithms. Such control scenarios are often observed in the neural control of vertebrate musculoskeletal systems. The study of these control mechanisms will provide insights into the control of high-dimensional, overactuated systems. The coordination of actuators, known as muscle synergies in neuromechanics, is considered a presumptive mechanism that simplifies the generation of motor commands. The dynamical structure of a system is the basis of its function, allowing us to derive a synergistic representation of actuators. Motivated by this theory, we propose the *Dynamical Synergistic Representation (DynSyn)* algorithm. DynSyn aims to generate synergistic representations from dynamical structures and perform task-specific, state-dependent adaptation to the representations to improve motor control. We demonstrate DynSyn's efficiency across various tasks involving different musculoskeletal models, achieving state-of-the-art sample efficiency and robustness compared to baseline algorithms. DynSyn generates interpretable synergistic representations that capture the essential features of dynamical structures and demonstrates generalizability across diverse motor tasks.

Modeling and control of the human musculoskele-tal system is important for understanding human motor functions, developing embodied intelligence, and optimizing human-robot interaction systems. However, current human musculoskeletal models are restricted to a limited range of body parts and often with a reduced number of muscles. There is also a lack of algorithms capable of controlling over 600 muscles to generate reasonable human movements. To fill this gap, we build a musculoskeletal model (MS-HUMAN-700) with 90 body segments, 206 joints, and 700 muscle-tendon units, allowing simulation of full-body dynamics and interaction with various devices. We develop a new algorithm using low-dimensional representation and hierarchical deep reinforcement learning to achieve state-of-the-art full-body control. We validate the effectiveness of our model and algorithm in simulations with real human locomotion data. The musculoskeletal model, along with its control algorithm, will be made available to the research community to promote a deeper understanding of human motion control and better design of interactive robots.Project page: https://lnsgroup.cc/research/MS-Human-700

In an effective process of cluster analysis, it is indispensable to validate the goodness of different partitions after clustering. Existing internal validation indexes are implemented based on distance and variance, which cannot catpure the real “density” of the cluster. Moreover the time complexity for distance-based indexes is usually too high to be applied for large datasets. Therefore, we propose a novel internal validation index based on the differential entropy, namedinternal purity(IP). The proposed IP index can effectively measure the purity of a cluster without using the external cluster information, and successfully overcome the drawbacks of existing internal indexes. Based on deep representation settings, where six powerful deep pretrained representation models are used, and nondeep representation settings, we use five basic crisp and fuzzy clustering algorithms to compare our index with 17 other well-known internal indexes on five text, five image datasets, and five tabular datasets. The results show that, for 105 test cases in total, our IP index can return the optimal clustering results in 61 cases while the second best index can merely report the optimal partition in 20 cases, which demonstrates the significant superiority of our IP index when validating the goodness of the clustering results. Moreover, theoretical analysis for the effectiveness and efficiency of the proposed index are also provided.