MUJICA: Multi-skill Unified Joint Integration of Control Architecture for Wheeled-Legged Robots

Wheeled-legged robots hold promise for traversing complex terrains and offer superior mobility compared to legged robots. However, wheeled-legged robots must effectively balance both wheeled driving and legged control. Furthermore, due to noisy proprioceptive sensing and real-world motor constraints, realizing robust and adaptive locomotion at peak performance of motors remains challenging. We propose the Multi-skill Unified Joint Integration of Control Architecture (MUJICA), a unified, fully proprioceptive control framework for wheeled-legged robots that integrates diverse low-level skills-including omnidirectional moving, high platform climbing, and fall recovery-within a single policy. All skills, distinguished by unique indicator variables, are trained jointly with accurate DC-motor constraint modeling. Additionally, a high-level skill selector is learned to dynamically choose the optimal skill based solely on proprioceptions, enabling adaptive responses to the surrounding environment. Therefore, MUJICA enhances sim-to-real robustness and enables seamless transitions across diverse locomotion modes, facilitating autonomous adjustment to the environment. We validate our framework in both simulation and real-world experiments on the Unitree Go2-W robot, demonstrating significant improvements in adaptability and task success in unstructured environments.

RENet: Fault-Tolerant Motion Control for Quadruped Robots via Redundant Estimator Networks under Visual Collapse

Vision-based locomotion in outdoor environments presents significant challenges for quadruped robots. Accurate environmental prediction and effective handling of depth sensor noise during real-world deployment remain difficult, severely restricting the outdoor applications of such algorithms. To address these deployment challenges in vision-based motion control, this letter proposes the Redundant Estimator Network (RENet) framework. The framework employs a dual-estimator architecture that ensures robust motion performance while maintaining deployment stability during onboard vision failures. Through an online estimator adaptation, our method enables seamless transitions between estimation modules when handling visual perception uncertainties. Experimental validation on a real-world robot demonstrates the framework's effectiveness in complex outdoor environments, showing particular advantages in scenarios with degraded visual perception. This framework demonstrates its potential as a practical solution for reliable robotic deployment in challenging field conditions. Project website: https://RENet-Loco.github.io/

A Robust Quadruped Robot with Twisting Waist for Flexible Motions

The waist plays a crucial role in the agile movement of many animals in nature. It provides the torso with additional degrees of freedom and flexibility, inspiring researchers to incorporate this biological feature into robotic structures to enhance robot locomotion. This paper presents a cost-effective and low-complexity waist mechanism integrated into the structure of the open-source robot solo8, adding a new degree of freedom (DOF) to its torso. We refer to this novel robot as solo9. Additionally, we propose a full-body control method for the waist-equipped quadruped robot based on generative adversarial imitation learning (GAIL). During training, the discriminator is used as input for iterative optimization of the policy and dataset, enabling solo9 to achieve flexible steering maneuvers across various gaits. Extensive tests of solo9's steering capabilities, terrain adaptability, and robustness are conducted in both simulation and real-world scenarios, with detailed comparisons to solo8 and solo12, demonstrating the effectiveness of the control algorithm and the advantages of the waist mechanism.