This is the GitHub organization home page for the Representing and Manipulating Deformable Linear Objects (RMDLO) project from the University of Illinois at Urbana-Champaign (UIUC). The project studies robotic manipulation, grasp planning, tactile sensing, segmentation, simulation, and tracking of wires and ropes.
Our growing software stack includes code for controlling two ABB IRB120 6DOF robotic manipulators, the OnRobot 2FG7 gripper, the Intel RealSense d435 camera, the Luxonis Oak-D Pro camera, and the DIGIT tactile sensor. For any project-related inquiries or requests, please contact Holly Dinkel [[email protected]].
Hameed Abdul-Rashid, Mårten Björkman, Marcel Büsching, Timothy Bretl, Brian Coltin, Holly Dinkel, Achintya Gahalaut, Naixiang (Gabe) Gao, Jagriti Jain, Danica Kragic, Alberta Longhini, Zikun (Tony) Liu, Raghavendra (Raghav) Navaratna, Florian Pokorny, Bhumsitt (Bose) Pramuanpornsatid, Yash Rathod, Fuad Samhouri, Trey Smith, Minh Quang Ta, Jingyi Xiang, Dan Yang, Muhammad Zahid, Harry Zhao
H. Dinkel, M. Büsching, A. Longhini, B. Coltin, T. Smith, D. Kragic, M. Björkman, and T. Bretl (2025) "DLO-Splatting: Tracking Deformable Linear Objects Using 3D Gaussian Splatting," IEEE ICRA 5th Workshop on Reflections on Representations and Manipulating Deformable Objects. [Paper] [Code] [Poster] [Video]
H. Dinkel, R. Navaratna, J. Xiang, B. Coltin, T. Smith, and T. Bretl (2024) "KnotDLO: Toward Interpretable Knot Tying," ICRA Workshop on 3D Visual Representations for Robot Manipulation. [Paper] [Code] [Poster] [Video]
J. Xiang, H. Dinkel, H. Zhao, N. Gao, B. Coltin, T. Smith, and T. Bretl (2023) "TrackDLO: Tracking Deformable Linear Objects Under Occlusion with Motion Coherence," IEEE Robotics and Automation: Letters. [Paper] [Code] [Poster] [Video]
J. Xiang and H. Dinkel (2023) "Simultaneous Shape Tracking of Multiple Deformable Linear Objects with Global-Local Topology Preservation," IEEE ICRA Workshop on Deformable Objects. [Paper] [Code] [Poster] [Video]
H. Dinkel, J. Xiang, H. Zhao, B. Coltin, T. Smith, and T. Bretl (2022) "Wire Point Cloud Instance Segmentation from RGBD Imagery with Mask R-CNN," IEEE ICRA Workshop on Deformable Objects. [Paper] [Code] [Video] [Supplementary Video]
This work is supported through NASA Space Technology Graduate Research Opportunity award 80NSSC21K1292, U.S. Department of Education Graduate Assistance in Areas of National award P200A180050-19, a P.E.O. International Scholar Award, a Zonta International Amelia Earhart Fellowship, an Illinois Space Grant Consortium Graduate Research Fellowship, the Illinois Space Grant Consortium Undergraduate Research Opportunity Program, the UIUC Promoting Undergraduate Research in Engineering program, the UIUC Office of Undergraduate Research, the UIUC Department of Aerospace Engineering, the UIUC Coordinated Science Laboratory, the UIUC Department of Electrical and Computer Engineering, and the UIUC Siebel School of Computing and Data Science.
