Sampling-based Planning of In-Hand Manipulation with External Pushes

By Nikhil Chavan Dafle, Alberto Rodriguez. 2017

50 citations recorded by the source corpus.

This paper presents a sampling-based planning algorithm for in-hand manipulation of a grasped object using a series of external pushes. A high-level sampling-based planning framework, in tandem with a low-level inverse contact dynamics solver, effectively explores the space of continuous pushes with discrete pusher contact switch-overs. We model the frictional interaction between gripper, grasped object, and pusher, by discretizing complex surface/line contacts into arrays of hard frictional point contacts. The inverse dynamics problem of finding an instantaneous pusher motion that yields a desired instantaneous object motion takes the form of a mixed nonlinear complementarity problem. Building upon this dynamics solver, our planner generates a sequence of pushes that steers the object to a goal grasp. We evaluate the performance of the planner for the case of a parallel-jaw gripper manipulating different objects, both in simulation and with real experiments. Through these examples, we highlight the important properties of the planner: respecting and exploiting the hybrid dynamics of contact sticking/sliding/rolling and a sense of efficiency with respect to discrete contact switch-overs.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering

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Citations in the corpus

Solving Rubik's Cube with a Robot Hand (2019, background): “This includes rolling [10, 41, 42, 15, 26], sliding [15, 100], finger gaiting [42], finger tracking [90], pushing [24], and re-grasping [109, 25].”
Learning dexterous in-hand manipulation (2018, background): “This includes rolling [8, 22, 23, 9, 13], sliding [9, 59], finger gaiting [23], finger tracking [51], pushing [11], and re-grasping [65, 12].”
Realtime State Estimation with Tactile and Visual Sensing. Application to Planar Manipulation (2017, methodology): “In the future, we would like to (1) test our estimation in the context of a pushing controller [23] for reactive manipulation, which has been tested with accurate ground truth feedback from an external tracking system, and (2) generalize to 3D tasks such as prehensile manipulation [24, 25, 26].”
Stable Prehensile Pushing: In-Hand Manipulation with Alternating Sticking Contacts (2017, methodology): “Both these routines are directly adopted from RRT ∗ architecture originally proposed in [21] and also used in our previous work [2].”