TrajBooster：首个全身人行操作VLA方案，跨构型解决数据难题（代码全开源）

Core Insights - The article discusses the TrajBooster framework, which aims to enhance the capabilities of humanoid robots by utilizing a trajectory-centric learning approach, enabling them to perform complex household tasks with minimal training data [2][40]. Group 1: Research Background and Challenges - The development of humanoid robots faces two main challenges: the unique difficulties of maintaining dynamic balance while performing upper body tasks, and the scarcity of high-quality training data necessary for effective VLA model training [3][4]. - Existing methods rely on expensive equipment and expert operators, resulting in limited data sets that do not adequately cover the diverse action spaces required for humanoid robots [4]. Group 2: TrajBooster Framework - TrajBooster utilizes a three-step process: real trajectory extraction, simulation redirection, and dual-stage fine-tuning, allowing for the conversion of extensive wheeled robot data into effective training resources for bipedal robots [5][40]. - The framework significantly reduces the dependency on costly data from similar robot types, enabling zero-shot skill transfer and improving the robustness and generalization of the VLA models [2][5]. Group 3: Methodology - The framework begins with extracting real trajectories from the Agibot-World Beta dataset, which contains over 1 million real robot trajectories, and then maps this data to the Unitree G1 robot's operational space [7][9]. - A hierarchical composite model is employed to decouple control into upper and lower body systems, enhancing the efficiency of whole-body manipulation [11][12]. Group 4: Experimental Results - TrajBooster demonstrated superior performance in various tasks, achieving the lowest position error (2.851 cm) and rotation error (6.231 degrees) in mobile scenarios, validating the advantages of hierarchical training and coordinated online DAgger [27]. - The framework's ability to adapt to unseen tasks was evidenced by its success in a "water transfer" task, which was not included in the training data, showcasing improved generalization capabilities [39][40]. Group 5: Limitations and Future Directions - The current implementation is limited by the precision of the Unitree Dex-3 hand, which only supports simple grasping tasks; future work will focus on integrating dexterous hands with tactile sensing for more complex manipulations [41]. - There is a need to address the visual input discrepancies and expand the framework to include mobile manipulation data, as the current research is primarily focused on static tasks [43][44].