Non-prehensile manipulation such as pushing is typically subject to uncertain, non-smooth dynamics. However, modeling the uncertainty of the dynamics typically results in intractable belief dynamics, making data-efficient planning under uncertainty difficult. This article focuses on the problem of efficiently generating robust open-loop pushing plans. First, we investigate how the belief over object configurations propagates through quasi-static contact dynamics. We exploit the simplified dynamics to predict the variance of the object configuration without sampling from a perturbation distribution. In a sampling-based trajectory optimization algorithm, the gain of the variance is constrained in order to enforce robustness of the plan. Second, we propose an informed trajectory sampling mechanism for drawing robot trajectories that are likely to make contact with the object. This sampling mechanism is shown to significantly improve chances of finding robust solutions, especially when making-and-breaking contacts is required. We demonstrate that the proposed approach is able to synthesize bi-manual pushing trajectories, resulting in successful long-horizon pushing maneuvers without exteroceptive feedback such as vision or tactile feedback. We furthermore deploy the proposed approach in a model-predictive control scheme, demonstrating additional robustness against unmodeled perturbations.

翻译：非抓取式操作（如推动）通常受不确定、非光滑动力学影响。然而，对动力学不确定性的建模往往导致难以处理的信念动力学，使得在不确定性下进行数据高效规划变得困难。本文聚焦于高效生成鲁棒开环推动规划的问题。首先，我们研究了物体构型的信念如何通过准静态接触动力学传播。利用简化动力学，我们无需从扰动分布中采样即可预测物体构型的方差。在基于采样的轨迹优化算法中，对方差的增益施加约束以增强规划的鲁棒性。其次，我们提出了一种感知式轨迹采样机制，用于生成可能与物体发生接触的机器人轨迹。该采样机制被证明能显著提高找到鲁棒解的概率，尤其是在需要建立和断开接触的场景中。我们证明所提方法能够合成双手推动轨迹，实现无需视觉或触觉等外感受反馈的长时程推动操作。此外，我们将所提方法部署于模型预测控制框架中，展示了对未建模扰动的额外鲁棒性。