用三维网格约束的Transformer生成机器人轨迹，更准更可靠。
PathFormer: A Transformer with 3D Grid Constraints for Digital Twin Robot-Arm Trajectory Generation
- 用位置/物体/时间三重网格编码运动，强制合法移动
- 90.4%的F1分数，97.5%抓取成功率，99.99%路径合法
- 适合需要可解释性与真实世界部署的机器人任务
机器人手臂需精准的任务感知轨迹规划，但忽略运动结构的序列模型常生成无效或低效动作。我们提出一种路径式Transformer，采用3维网格（位置/物体/时间）表示并结合约束掩码解码，强制实现格子邻接移动和工作空间限制，同时推理任务图与动作顺序。在53,755条轨迹上训练（80%训练 / 20%验证），模型与真实轨迹高度一致：逐步准确率89.44%，精确率93.32%，召回率89.44%，F1值90.40%，且99.99%路径由构造保证合法。部署于xArm Lite 6机械臂并结合深度相机数字孪生系统，在受控测试中达到最高97.5%到达率与92.5%抓取成功率，60个语言指令任务中端到端成功率达86.7%，通过局部重新定位吸收滑动与遮挡，无需全局重规划。结果表明，路径结构化表示使Transformer能生成准确、可靠、可解释的机器人轨迹，融合图规划与序列学习，为通用操作与仿真到现实迁移提供实用基础。
原文摘要 · Abstract (English)
Robotic arms require precise, task-aware trajectory planning, yet sequence models that ignore motion structure often yield invalid or inefficient executions. We present a Path-based Transformer that encodes robot motion with a 3-grid (where/what/when) representation and constraint-masked decoding, enforcing lattice-adjacent moves and workspace bounds while reasoning over task graphs and action order. Trained on 53,755 trajectories (80% train / 20% validation), the model aligns closely with ground truth -- 89.44% stepwise accuracy, 93.32% precision, 89.44% recall, and 90.40% F1 -- with 99.99% of paths legal by construction. Compiled to motor primitives on an xArm Lite 6 with a depth-camera digital twin, it attains up to 97.5% reach and 92.5% pick success in controlled tests, and 86.7% end-to-end success across 60 language-specified tasks in cluttered scenes, absorbing slips and occlusions via local re-grounding without global re-planning. These results show that path-structured representations enable Transformers to generate accurate, reliable, and interpretable robot trajectories, bridging graph-based planning and sequence-based learning and providing a practical foundation for general-purpose manipulation and sim-to-real transfer.
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