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A fully automated and reliable picking of a diverse range of previously unseen objects in clutter is a challenging problem. This becomes even more difficult given the inherent uncertainty in sensing, control, and interaction physics. This paper presents a robust method for stable and collision-free grasp planning, given a cluttered heap of novel objects of different varieties. Our grasp planning pipeline leverages a novel grasp pose ranking method and a pose refinement method that ensures collision-free gripping and stable contact between gripper-fingers and the target object. Often, a grasp planning algorithm may not be able to find a valid grasp pose due to the tightly-packed configuration of the objects. In such situations, our method directs the robot to perform a clutter removal action using a linear push policy. On a physical robot with a two-fingered parallel-jaw gripper and a depth sensor, our method can consistently clear up the pile of up to 20 objects with 95% reliability.
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Prem Raj
Ashish Kumar
Vipul Sanap
Indian Institute of Technology Kanpur
Indian Institute of Technology Mandi
Intel (India)
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Raj et al. (Sat,) studied this question.
www.synapsesocial.com/papers/6a0ef2561c5e2d2319fa21a9 — DOI: https://doi.org/10.1109/case49997.2022.9926708