Grasping with application to an autonomous checkout robot

By E. Klingbeil, Deepak Rao, Blake Carpenter, Varun Ganapathi, Andrew Y. Ng, Oussama Khatib. 2011

102 citations recorded by the source corpus.

In this paper, we present a novel grasp selection algorithm to enable a robot with a two-fingered end-effector to autonomously grasp unknown objects. Our approach requires as input only the raw depth data obtained from a single frame of a 3D sensor. Additionally, our approach uses no explicit models of the objects and does not require a training phase. We use the grasping capability to demonstrate the application of a robot as an autonomous checkout clerk. To perform this task, the robot must identify how to grasp an object, locate the barcode on the object and read the numeric code. We evaluate our grasping algorithm in experiments where the robot was required to autonomously grasp unknown objects. The robot achieved a success of 91.6%in grasping novel objects. We performed two sets of experiments to evaluate the checkout robot application. In the first set, the objects were placed in many orientations in front of the robot one at a time. In the second set, the objects were placed several at a time with varying amounts of clutter. The robot was able to autonomously grasp and scan the objects in 49/50 of the single-object trials and 46/50 of the cluttered trials.

Subfields: Biomedical Engineering, Control and Systems Engineering, Robotics & Control

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Citations in the corpus

Learning Trajectory Preferences for Manipulators via Iterative Improvement (2013, neutral): “Our work complements previous works on grasping items [30, 21], pick and place tasks [11], and detecting bar code for grocery checkout [16].”
Data-Driven Grasp Synthesis—A Survey (2013, methodology): “The main idea presented by Klingbeil et al. [117] is to search for a pattern in the scene that is similar to the 2D cross section of the robotic gripper interior.”
Learning preferences for manipulation tasks from online coactive feedback (2015, result): “Our work complements previous works on grasping items [49, 34], pick and place tasks [23], and detecting bar codes for grocery checkout [29].”
6-DOF Grasping for Target-driven Object Manipulation in Clutter (2019, methodology): “Those learningbased approaches that plan full grasp poses are either based on hand-crafted features ([32]–[34]) or have non-learned components [11].”
Learning of grasp selection based on shape-templates (2013, background): “metrical features which indicate good grasps as for example in [17,27,24,14,34].”
Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks (2020, background): “Some work assumes that a sturdy, brown bag is already open for item insertion, as with a grocery checkout robot [36], or uses reinforcement learning for zipping bags [26] in constrained setups.”