Core Viewpoint - The article highlights the launch of the Tien Kung-Lab, an open-source motion control framework for humanoid robots, which aims to enhance their adaptability and task execution capabilities in complex environments [1][3]. Group 1: Tien Kung-Lab Overview - Tien Kung-Lab is an open-source reinforcement learning motion control algorithm framework developed based on Isaaclab, integrating advanced reinforcement learning techniques and human motion data [3]. - The framework allows humanoid robots to achieve natural, efficient, and stable motion control, demonstrated by the Tian Gong Ultra winning the first humanoid robot marathon with a time of 2 hours, 40 minutes, and 42 seconds [3][7]. - The framework combines the advantages of imitation learning and reinforcement learning, utilizing an Adversarial Motion Prior (AMP) reward mechanism to enhance stability and generalization in complex terrains [3][4]. Group 2: Technical Innovations - The open-source framework improves ray tracing technology for rapid and accurate acquisition of depth maps and LiDAR point clouds, enabling end-to-end training from perception to motion in simulation environments [4]. - Tien Kung-Lab supports high-fidelity physics engine MuJoCo for Sim2Sim cross-validation, facilitating seamless transitions from training to validation [4]. - The framework has been successfully implemented on the Tian Gong 2.0 full-size humanoid robot, achieving high generalization performance in walking and running [4][5]. Group 3: Performance and Achievements - The Tian Gong Ultra demonstrated robust motion control during the marathon, effectively navigating various real-world obstacles such as bumps, dips, and speed bumps [7]. - The Tian Gong 1.0 lite, released in April 2024, was the first full-size electric humanoid robot capable of running on diverse terrains, showcasing significant advancements in motion control technology [9]. - In the 2024 Zhongguancun Bionic Robot Competition, the Tian Gong Ultra won multiple championships, including a running event with a time of 15.66 seconds and an obstacle course, highlighting its superior mobility and task execution capabilities [11]. Group 4: Future Directions - The company aims to advance towards "small brain-level intelligence," focusing on general mobility operations and complex motion control in dynamic environments [14]. - Continuous breakthroughs in motion control will establish a robust "motion intelligence hub" for humanoid robots, enabling them to autonomously navigate and perform tasks in real-world scenarios [14]. - The open-source nature of Tien Kung-Lab is expected to significantly shorten the time required for humanoid robots to achieve high-speed generalized movement across various terrains, laying the groundwork for their deployment in extreme environments [13][14].