Pushing Revisited: Differential Flatness, Trajectory Planning and Stabilization

Zhou, Jiaji; Mason, Matthew T.

doi:10.1007/978-3-030-28619-4_44

Cited by 24 publications

(40 citation statements)

References 15 publications

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“…Howe and Cutkosky [15], Xydas and Kao [33] showed that an ellipsoidal approximation allows for a simpler representation of the limit surface geometry. In this paper we will assume an ellipsoidal approximation of the limit surface, which has been shown to be computationally efficient for simulating and planning pushing motions [21,6,25,34].…”

Section: A Limit Surfacementioning

confidence: 99%

In-Hand Manipulation via Motion Cones

Dafle

Holladay

Rodríguez

2018

Robotics: Science and Systems XIV

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In this paper, we present the mechanics and algorithms to compute the set of feasible motions of an object pushed in a plane. This set is known as the motion cone and was previously described for non-prehensile manipulation tasks in the horizontal plane. We generalize its geometric construction to a broader set of planar tasks, where external forces such as gravity influence the dynamics of pushing, and prehensile tasks, where there are complex interactions between the gripper, object, and pusher. We show that the motion cone is defined by a set of low-curvature surfaces and provide a polyhedral cone approximation to it. We verify its validity with 2000 pushing experiments recorded with motion tracking system.Motion cones abstract the algebra involved in simulating frictional pushing by providing bounds on the set of feasible motions and by characterizing which pushes will stick or slip. We demonstrate their use for the dynamic propagation step in a sampling-based planning algorithm for in-hand manipulation. The planner generates trajectories that involve sequences of continuous pushes with 5-1000x speed improvements to equivalent algorithms. Video Summary -youtu.be/tVDO8QMuYhc

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Section: A Limit Surfacementioning

confidence: 99%

In-Hand Manipulation via Motion Cones

Dafle

Holladay

Rodríguez

2018

Robotics: Science and Systems XIV

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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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“…Lynch and Mason (1996) and Zhou and Mason (2017) introduced motion planning algorithms to find open-loop robotic trajectories achieving a target object pose. Key to the success of these executions are the assumptions that sticking interactions are maintained for the entirety of the push and that the object remains unperturbed during the execution.…”

Section: Related Workmentioning

confidence: 99%

Reactive planar non-prehensile manipulation with hybrid model predictive control

Hogan

Rodríguez

2020

The International Journal of Robotics Research

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This article presents an offline solution and online approximation to the hybrid control problem of planar non-prehensile manipulation. Hybrid dynamics and underactuation are key characteristics of this task that complicate the design of feedback controllers. We show that a model predictive control approach used in tandem with integer programming offers a powerful solution to capture the dynamic constraints associated with the friction cone as well as the hybrid nature of contact. We introduce the Model Predictive Controller with Learned Mode Scheduling (MPC-LMS), which leverages integer programming and machine learning techniques to effectively deal with the combinatorial complexity associated with determining sequences of contact modes. We validate the controller design through a numerical simulation study and with experiments on a planar manipulation setup using an industrial ABB IRB 120 robotic arm. Results show that the proposed algorithm achieves closed-loop tracking of a nominal trajectory by reasoning in real-time across multiple contact modalities.

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Pushing Revisited: Differential Flatness, Trajectory Planning and Stabilization

Cited by 24 publications

References 15 publications

In-Hand Manipulation via Motion Cones

In-Hand Manipulation via Motion Cones

Online Hybrid Motion Planning for Dyadic Collaborative Manipulation via Bilevel Optimization

Reactive planar non-prehensile manipulation with hybrid model predictive control

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