This website accompanies papers related to the study of Mechanical Search from the Stanford Vision and Learning Lab and UC Berkeley's AUTOLAB. Click on their names below to see the abstract, papers, accompanying videos, and qualitative result videos.

Mechanical Search: Multi-Step Retrieval of a Target Object Occluded by Clutter

ICRA 2019

Visuomotor Mechanical Search: Learning to Retrieve Target Objects in Clutter

IROS 2020

Multi-Layer Semantic and Geometric Modeling with Neural Message Passing in 3D Scene Graphs for Hierarchical Mechanical Search

ICRA 2021, under submission

Mechanical Search on Shelves using Lateral Access X-RAY

ICRA 2021, under submission

Acknowledgements

This work is partially supported by a Google Focused Research Award and was performed jointly at the AUTOLAB at UC Berkeley and at the Stanford Vision & Learning Lab, in affiliation with the Berkeley AI Research (BAIR) Lab, Berkeley Deep Drive (BDD), the Real-Time Intelligent Secure Execution (RISE) Lab, and the CITRIS "People and Robots" (CPAR) Initiative. Authors were also supported by the SAIL-Toyota Research initiative, the Scalable Collaborative Human-Robot Learning (SCHooL) Project, the NSF National Robotics Initiative Award 1734633, and in part by donations from Siemens, Google, Amazon Robotics, Toyota Research Institute, Autodesk, ABB, Knapp, Loccioni, Honda, Intel, Comcast, Cisco, Hewlett-Packard and by equipment grants from PhotoNeo, and NVidia. This article solely reflects the opinions and conclusions of its authors and do not reflect the views of the Sponsors or their associated entities. We thank our colleagues who provided helpful feedback, code, and suggestions, in particular Jeff Mahler.