AI 中文总结
针对软体机器人全身抓取与投掷难题，提出基于结果的驱动空间优化框架，结合CMA-ES与学习初始化，在仿真和硬件上分别实现98.4%和100%的成功率。
AI 中文摘要
软体连续体机器人可以利用分布式柔顺性进行全身操作，但通过变化的接触来综合行为仍然困难。我们通过基于结果的驱动空间优化，解决了从初始未抓取状态开始的全身抓取和拾取-投掷问题。抓取通过尖端角扫掠和身体-物体包围程度来量化，而投掷进一步结合了释放方向对齐和最小释放速度。这些目标使得抓取、加速和释放能够从柔顺交互中涌现，而无需规定接触力、接触位置或身体构型。由于所得的驱动到结果映射是非光滑的，我们在优化-学习-精炼框架内使用无导数的CMA-ES。CMA-ES为采样条件生成解决方案，任务条件预测器学习热启动，CMA-ES为未见条件精炼这些解决方案。在仿真中，该方法实现了492/500次成功抓取（98.4%），并在三个方向的投掷试验中分别达到98%、97%和94%的成功率。学习初始化将抓取成功率从78.6%提高到98.4%，同时将CMA-ES中的中位滚动次数从1184减少到816。硬件实验在50次执行中实现了100%的抓取成功率，在30次执行中实现了100%的拾取-投掷成功率，每个方向重复10次。这些仿真和硬件结果共同证明了所提出框架在模拟和物理全身操作任务中的有效性。
英文摘要
Soft continuum robots can exploit distributed compliance for whole-body manipulation, but synthesizing behavior through changing contacts remains difficult. We address whole-body grasping and pick-and-throw from an initially ungrasped state through outcome-based actuation-space optimization. Grasping is quantified by tip angular sweep and body-object enclosure, while throwing further incorporates release-direction alignment and minimum release speed. These objectives allow grasping, acceleration, and release to emerge from compliant interaction without prescribing contact forces, contact locations, or body configurations. Because the resulting actuation-to-outcome mapping is nonsmooth, we utilize derivative-free CMA-ES within an optimize-learn-refine framework. CMA-ES generates solutions for sampled conditions, a task-conditioned predictor learns warm starts, and CMA-ES refines them for unseen conditions. In simulation, the method achieves 492/500 successful grasps (98.4%) and success rates of 98%, 97%, and 94% across three directional throwing trials. Learned initialization increases grasping success from 78.6% to 98.4% while reducing the median rollout count from 1184 to 816 in CMA-ES. Hardware experiments achieve a 100% grasping success rate across 50 executions and a 100% pick-and-throw success rate across 30 executions, with 10 repetitions per direction. Together, these simulation and hardware results demonstrate the effectiveness of the proposed framework across both simulated and physical whole-body manipulation tasks.
