用扩散模型规划柔性机械臂穿孔轨迹，减少碰撞并提升成功率。
THREAD: Trajectory Planning for Hybrid Rigid-Soft Manipulators with Environment-Aware Diffusion
- 基于扩散模型生成符合物理规律的混合刚柔机械臂轨迹
- 92.4%任务成功率，碰撞次数减少5倍
- 适合需要高精度穿孔操作的机器人应用
在狭小环境中的操作（如将机械臂穿过狭窄孔洞）仍是基础挑战，尤其对传统刚性机器人而言。混合刚柔机械臂虽有潜力，但面临双重规划难题：自由空间中可行的构型在接触环境后不可行，且刚性与柔性部分独立规划忽略了其运动学耦合。我们提出THREAD，首个面向混合操作的扩散轨迹规划方法，通过学习基于局部环境几何的物理可实现构型先验，并引入融合曲率、平滑度和碰撞约束的物理启发损失函数，联合优化两部分。在仿真中训练后，THREAD实现92.4%的任务成功率，碰撞次数比最强基线减少5倍。我们展示了跨实体的现实世界迁移能力，仅需少量在线更新，即可成功穿过最小为软段直径1.3倍的孔洞。
原文摘要 · Abstract (English)
Manipulation in confined environments, such as threading a manipulator through narrow apertures, remains a fundamental challenge, especially for conventional rigid robots. Hybrid rigid-soft manipulators offer promise but face two compounding planning challenges: backbone shapes feasible in free space become infeasible under environmental contact, and planning rigid and soft segments independently ignores their kinematic coupling. We present THREAD, the first diffusion-based trajectory planner for hybrid manipulation, learning a generative prior over physically realizable backbone trajectories conditioned on local environment geometry, with physics-inspired losses encoding curvature, smoothness, and collision constraints jointly across both segments. Trained in simulation, THREAD achieves 92.4% task success with 5x fewer collisions than the strongest baseline. We show cross-embodiment real-world transfer with minimal online updates, successfully threading through apertures as small as 1.3x the soft segment diameter.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。