用多头技能变换器实现长时序灵巧操作的自动编排
MuST: Multi-Head Skill Transformer for Long-Horizon Dexterous Manipulation with Skill Progress
- 通过多头结构学习并串联多个动作原语，实现复杂操作序列生成
- 引入技能进度值，动态指导下一动作选择，提升执行连贯性
- 支持技能扩展与多任务序列管理，适用于真实世界灵巧操作
机器人抓取与包装任务需要灵巧操作能力，如重新排列物体以建立良好抓取姿态，或推挤放置物品实现紧密堆叠。这些任务因动作复杂且多变而对机器人构成挑战。为应对长时序任务的学习与执行难题，本文提出多头技能变换器（MuST）框架。该模型通过学习并顺序组合多个运动原语（技能），使机器人能够有效执行复杂动作序列。MuST为每个技能引入“进度值”，指导机器人选择下一步动作，确保技能间平滑过渡。此外，模型具备扩展技能集和高效管理子任务序列的能力。在仿真与真实环境中的大量实验表明，MuST显著提升了机器人执行长时序灵巧操作任务的能力。
原文摘要 · Abstract (English)
Robot picking and packing tasks require dexterous manipulation skills, such as rearranging objects to establish a good grasping pose, or placing and pushing items to achieve tight packing. These tasks are challenging for robots due to the complexity and variability of the required actions. To tackle the difficulty of learning and executing long-horizon tasks, we propose a novel framework called the Multi-Head Skill Transformer (MuST). This model is designed to learn and sequentially chain together multiple motion primitives (skills), enabling robots to perform complex sequences of actions effectively. MuST introduces a "progress value" for each skill, guiding the robot on which skill to execute next and ensuring smooth transitions between skills. Additionally, our model is capable of expanding its skill set and managing various sequences of sub-tasks efficiently. Extensive experiments in both simulated and real-world environments demonstrate that MuST significantly enhances the robot's ability to perform long-horizon dexterous manipulation tasks.
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