A Probabilistic Planning Framework for Planar Grasping Under Uncertainty

Zhou, Jiaji; Paolini, Robert; Johnson, Aaron M.; Bagnell, J. Andrew; Mason, Matthew T.

doi:10.1109/lra.2017.2720845

Cited by 37 publications

(30 citation statements)

References 25 publications

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“…A practical example of using the knowledge of uncertainty in planar manipulation was introduced by Zhou et al [21]. They proposed a probabilistic algorithm that generates sequential actions to iteratively reduce uncertainty of objects in the plane, before grasping it with a parallel jaw gripper.…”

Section: Uncertainty Modelingmentioning

confidence: 99%

Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing

Ajay

Fazeli

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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An efficient, generalizable physical simulator with universal uncertainty estimates has wide applications in robot state estimation, planning, and control. In this paper, we build such a simulator for two scenarios, planar pushing and ball bouncing, by augmenting an analytical rigid-body simulator with a neural network that learns to model uncertainty as residuals. Combining symbolic, deterministic simulators with learnable, stochastic neural nets provides us with expressiveness, efficiency, and generalizability simultaneously. Our model outperforms both purely analytical and purely learned simulators consistently on real, standard benchmarks. Compared with methods that model uncertainty using Gaussian processes, our model runs much faster, generalizes better to new object shapes, and is able to characterize the complex distribution of object trajectories.

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Section: Uncertainty Modelingmentioning

confidence: 99%

Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing

Ajay

Fazeli

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…The limit surface concept was introduced in [32] and used in [33] to model the dynamics of planar pushing. These concepts map contact point's motion to object's motion, and have been used [34]- [36] to address planning and control for planar pushing. Also, recently they were generalized to a broader set of planar tasks [37].…”

Section: B Hybrid Motion With Contact Changesmentioning

confidence: 99%

Online Hybrid Motion Planning for Dyadic Collaborative Manipulation via Bilevel Optimization

et al. 2020

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Effective collaboration is based on online adaptation of one's own actions to the actions of their partner. This article provides a principled formalism to address online adaptation in joint planning problems such as Dyadic collaborative Manipulation (DcM) scenarios. We propose an efficient bilevel formulation that combines graph search methods with trajectory optimization, enabling robotic agents to adapt their policy on-the-fly in accordance to changes of the dyadic task. This method is the first to empower agents with the ability to plan online in hybrid spaces; optimizing over discrete contact locations, contact sequence patterns, continuous trajectories, and force profiles for co-manipulation tasks. This is particularly important in large object co-manipulation that requires changes of grasp-holds and plan adaptation. We demonstrate in simulation and with robot experiments the efficacy of the bilevel optimization by investigating the effect of robot policy changes in response to real-time alterations of the dyadic goals, eminent grasp switches, as well as optimal dyadic interactions to realize the joint task. Index Terms-Dual arm manipulation (DaM), manipulation planning, optimization and optimal control, physical human-robot interaction. I. INTRODUCTION D YADIC collaborative Manipulation (DcM) is a term we use to refer to a set of two individuals jointly manipulating an object, as shown in Fig. 1. The two individuals partner together to form a distributed system, augmenting their Manuscript

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“…Goldberg [ 9 ] planned sequences of pushes and squeezes to orient planar polygons up to symmetry. Zhou et al [ 10 ] plans similar sequences based on an efficient simulation of planar pushing. Berretty et al [ 11 ] proposed an approach of executing pulling actions using overhead fingers for object reorientation.…”

Section: Introductionmentioning

confidence: 99%

Sensorless Pose Determination Using Randomized Action Sequences

Mannam

Volkov

Paolini

et al. 2019

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This paper is a study of 2D manipulation without sensing and planning, by exploring the effects of unplanned randomized action sequences on 2D object pose uncertainty. Our approach follows the work of Erdmann and Mason's sensorless reorienting of an object into a completely determined pose, regardless of its initial pose. While Erdmann and Mason proposed a method using Newtonian mechanics, this paper shows that under some circumstances, a long enough sequence of random actions will also converge toward a determined final pose of the object. This is verified through several simulation and real robot experiments where randomized action sequences are shown to reduce entropy of the object pose distribution. The effects of varying object shapes, action sequences, and surface friction are also explored.

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A Probabilistic Planning Framework for Planar Grasping Under Uncertainty

Cited by 37 publications

References 25 publications

Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing

Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing

Online Hybrid Motion Planning for Dyadic Collaborative Manipulation via Bilevel Optimization

Sensorless Pose Determination Using Randomized Action Sequences

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