Robust Pushing: Exploiting Quasi-static Belief Dynamics and Contact-informed Optimization (2404.02795v2)

Abstract: 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.

• The paper introduces a two-stage approach that predicts and controls belief over an object’s configuration to enable robust non-prehensile manipulation.
• It employs a quasi-static variance prediction model, reducing reliance on costly sampling methods during the trajectory planning process.
• It integrates contact-informed trajectory sampling within a stochastic optimization framework to improve pushing performance in uncertain environments.

Essay on "Planning for Robust Open-loop Pushing: Exploiting Quasi-static Belief Dynamics and Contact-informed Optimization"

The paper by Jankowski et al. investigates the intricate problem of generating robust open-loop pushing plans for robotic manipulation under uncertainty. The research focuses on non-prehensile manipulation methods, particularly pushing, which commonly involve uncertain and non-linear dynamics that are challenging to model accurately. Traditional approaches often rely on frequently updated feedback loops to manage such uncertainties. In contrast, this paper focuses on generating open-loop control trajectories by explicitly modeling uncertainty in the contact dynamics, allowing for robust execution without continual sensory feedback.

The cornerstone of this research is the two-stage contribution: the prediction and subsequent control of the belief over an object's configuration to achieve robust manipulation. By leveraging quasi-static models of contact dynamics, the authors attain predictions of the variance in object configurations without resorting to computationally expensive sampling methods. This approach integrates novel methods for predicting the gain in variance to constrain solutions within a sampling-based trajectory optimization framework, contributing to an enhanced ability to find robust manipulation plans.

A pivotal aspect of this work is the development of an informed trajectory sampling mechanism, which prioritizes robot trajectories that are predisposed to induce contact with the object. This mechanism is especially useful in scenarios requiring dynamic contact switching, enhancing the chances of deriving successful manipulation solutions. The methodology demonstrates significant improvements in generating bi-manual pushing trajectories capable of executing long-horizon maneuvers without relying on direct feedback from exteroceptive sensors like cameras or tactile arrays.

The paper offers detailed insights into variance prediction within the context of stochastic contact dynamics, advancing methods for predicting the variance through quasi-static approximations. This deterministic computation of variance contributes to the efficiency of planning algorithms as it circumvents the need for sampling-based perturbation assessments. The authors integrate this predictive capability within a via-point-based stochastic trajectory optimization framework (VP-STO), thereby enhancing its robustness by constraining solutions with predicted variance dynamics.

Experimental validation highlights the efficacy of the proposed approach in synthesizing manipulation behaviors that withstand variations in dynamic effects like friction, while maintaining operation in environments with uncertain initial conditions and model inaccuracies. A notable achievement is the algorithm's capability to maintain high performance in the absence of predefined manipulation primitives, thus pioneering an evolution beyond classical model-dependent strategies in contact-rich manipulation planning.

The paper's results have both practical and theoretical implications for the advancement of autonomous robotic operation in complex environments. Practically, the adoption of robust open-loop control could facilitate deployments in scenarios where sensory feedback is unreliable or delayed. Theoretically, this work lays a foundation for further exploration into the integration of stochastic control principles with contact dynamics, possibly extending to more dynamic and fast-paced manipulation tasks in future research.

As robotic systems continue to infiltrate unstructured environments, methodologies that optimize for robustness under uncertainty will likely grow in relevance and demand. This research provides an articulate framework for approaching such challenges, yielding robust manipulation trajectories driven by an insightful understanding of belief dynamics through contact. Future research could explore integrating this approach within broader autonomous systems, potentially enhancing adaptability and performance across diverse applications.