Abstract
In this chapter, we provide details of the system that was used for our participation in the Amazon Robotics Challenge 2017 held in Nagoya, Japan. Our hardware system comprised of an UR10 robot manipulator with an eye-in-hand 2D/3D vision system and a suction based gripper. Some of the novel contributions made in this work include (1) a Deep Learning based vision system for recognizing and segmenting products in a clutter; (2) a new geometry based grasping algorithm that can find graspable affordances in extreme clutter; (3) a hybrid two-finger gripper that combines both suction and gripping action; and (4) a system for automating the generation of annotated templates needed for training deep networks. The resulting system could achieve a pick rate of 2–3 objects per minute. As an outcome, the IITK-TCS team secured fifth position in the stow task, third position in the pick task and fourth position in the final round in the above challenge.
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Acknowledgements
We acknowledge the contribution of many members who worked for the IITK-TCS team. Some of the foremost members in this list include Ashish Kumar (IITK), Ravi Prakash (IITK), Siddharth (IITK), Mohit (IITK), Sharath Jotawar (TCS), Manish Soni (TCS), Prasun Pallav (TCS), Chandan K Singh (TCS), Venkat Raju (TCS) and Rajesh Sinha (TCS).
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Majumder, A., Kundu, O., Dutta, S., Kumar, S., Behera, L. (2020). Description of IITK-TCS System for ARC 2017. In: Causo, A., Durham, J., Hauser, K., Okada, K., Rodriguez, A. (eds) Advances on Robotic Item Picking. Springer, Cham. https://doi.org/10.1007/978-3-030-35679-8_10
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