Planning for Robust Open-loop Pushing: Exploiting Quasi-static Belief Dynamics and Contact-informed Optimization
arxiv(2024)
摘要
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.
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