用概率方法同时调度多个推手，实现高效抓握推送操作
Pushing Everything Everywhere All At Once: Probabilistic Prehensile Pushing
- 将环境推手建模为概率分布，通过熵最小化实现动态选择
- 计算速度比现有方法快8倍，成本降低20倍
- 已在仿真和真实Franka Panda机械臂上成功验证
我们研究抓握推送问题，即通过推动物体与环境交互来操控被握物体。提出一种高效的非线性轨迹优化方法，由精确的混合整数非线性规划松弛而来。核心思想是将外部推手（环境）建模为离散概率分布，而非二值变量，并最小化该分布的熵。概率重构使所有推手可同时使用，但在最优解下概率质量集中于单一推手。在抓握推送任务中，数值对比显示本方法比当前最先进的采样基基准快8倍，成本低20倍。最后，我们在仿真与真实Franka Panda机器人上成功实现了对多种物体的操控。补充材料见 https://probabilistic-prehensile-pushing.github.io/。
原文摘要 · Abstract (English)
We address prehensile pushing, the problem of manipulating a grasped object by pushing against the environment. Our solution is an efficient nonlinear trajectory optimization problem relaxed from an exact mixed integer non-linear trajectory optimization formulation. The critical insight is recasting the external pushers (environment) as a discrete probability distribution instead of binary variables and minimizing the entropy of the distribution. The probabilistic reformulation allows all pushers to be used simultaneously, but at the optimum, the probability mass concentrates onto one due to the entropy minimization. We numerically compare our method against a state-of-the-art sampling-based baseline on a prehensile pushing task. The results demonstrate that our method finds trajectories 8 times faster and at a 20 times lower cost than the baseline. Finally, we demonstrate that a simulated and real Franka Panda robot can successfully manipulate different objects following the trajectories proposed by our method. Supplementary materials are available at https://probabilistic-prehensile-pushing.github.io/.
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。