Andrew Lu

2025

1. RoboSoft

   

   Physics-Grounded Differentiable Simulation for Soft Growing Robots

   Lucas Chen^*, Yitian Gao^*, Sicheng Wang, Francesco Fuentes, Laura H. Blumenschein, and Zachary Kingston

   In IEEE-RAS International Conference on Soft Robotics

   To Appear

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   Soft-growing robots (i.e., vine robots) are a promising class of soft robots that allow for navigation and growth in tightly confined environments. However, these robots remain challenging to model and control due to the complex interplay of the inflated structure and inextensible materials, which leads to obstacles for autonomous operation and design optimization. Although there exist simulators for these systems that have achieved qualitative and quantitative success in matching high-level behavior, they still often fail to capture realistic vine robot shapes using simplified parameter models and have difficulties in high-throughput simulation necessary for planning and parameter optimization. We propose a differentiable simulator for these systems, enabling the use of the simulator "in-the-loop" of gradient-based optimization approaches to address the issues listed above. With the more complex parameter fitting made possible by this approach, we experimentally validate and integrate a closed-form nonlinear stiffness model for thin-walled inflated tubes based on a first-principles approach to local material wrinkling. Our simulator also takes advantage of data-parallel operations by leveraging existing differentiable computation frameworks, allowing multiple simultaneous rollouts. We demonstrate the feasibility of using a physics-grounded nonlinear stiffness model within our simulator, and how it can be an effective tool in sim-to-real transfer. We provide our implementation open source.

   @inproceedings{chengao2025diffsim,
     title = {Physics-Grounded Differentiable Simulation for Soft Growing Robots},
     author = {Chen, Lucas and Gao, Yitian and Wang, Sicheng and Fuentes, Francesco and Blumenschein, Laura H. and Kingston, Zachary},
     year = {2025},
     booktitle = {IEEE-RAS International Conference on Soft Robotics},
     eprint = {2501.17963},
     archiveprefix = {arXiv},
     primaryclass = {cs.RO},
     note = {To Appear},
   }

2018

1. Distributed Object Characterization with Local Sensing by a Multi-Robot System

   Golnaz Habibi, Sándor P. Fekete, Zachary Kingston, and James McLurkin

   In Distributed Autonomous Robotic Systems

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   This paper presents two distributed algorithms for enabling a swarm of robots with local sensing and local coordinates to estimate the dimensions and orientation of an unknown complex polygonal object, ie, its minimum and maximum width and its main axis. Our first approach is based on a robust heuristic of distributed Principal Component Analysis (DPCA), while the second is based on turning the idea of Rotating Calipers into a distributed algorithm (DRC). We simulate DRC and DPCA methods and test DPCA on real robots. The result show our algorithms successfully estimate the dimension and orientation of convex or concave objects with a reasonable error in the presence of noisy data.

   @incollection{habibi2018dars,
     author = {Habibi, Golnaz and Fekete, S{\'a}ndor P. and Kingston, Zachary and McLurkin, James},
     title = {Distributed Object Characterization with Local Sensing by a Multi-Robot System},
     booktitle = {Distributed Autonomous Robotic Systems},
     publisher = {Springer Proceedings in Advanced Robotics},
     year = {2018},
     editor = {Gro{\ss}, Roderich and Kolling, Andreas and Berman, Spring and Frazzoli, Emilio and Martinoli, Alcherio and Matsuno, Fumitoshi and Gauci, Melvin},
     volume = {6},
     pages = {205--218},
     doi = {10.1007/978-3-319-73008-0_15},
   }

2015

1. Pipelined Consensus for Global State Estimation in Multi-Agent Systems

   Golnaz Habibi, Zachary Kingston, Zijian Wang, Mac Schwager, and James McLurkin

   In Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems

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   This paper presents pipelined consensus, an extension of pair-wise gossip-based consensus, for multi-agent systems using mesh networks. Each agent starts a new consensus in each round of gossiping, and stores the intermediate results for the previous k consensus in a pipeline message. After k rounds of gossiping, the results of the first consensus are ready. The pipeline keeps each consensus independent, so any errors only persist for k rounds. This makes pipelined consensus robust to many real-world problems that other algorithms cannot handle, including message loss, changes in network topology, sensor variance, and changes in agent population. The algorithm is fully distributed and self-stabilizing, and uses a communication message of fixed size. We demonstrate the efficiency of pipelined consensus in two scenarios: computing mean sensor values in a distributed sensor network, and computing a centroid estimate in a multi-robot system. We provide extensive simulation results, and real-world experiments with up to 24 agents. The algorithm produces accurate results, and handles all of the disturbances mentioned above.

   @incollection{habibi2015aamas,
     author = {Habibi, Golnaz and Kingston, Zachary and Wang, Zijian and Schwager, Mac and McLurkin, James},
     title = {Pipelined Consensus for Global State Estimation in Multi-Agent Systems},
     booktitle = {Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems},
     publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
     year = {2015},
     pages = {1315--1323},
     isbn = {9781450334136},
     doi = {10.5555/2772879.2773320},
   }

2. ICRA

   Distributed Centroid Estimation and Motion Controllers for Collective Transport by Multi-Robot Systems

   Golnaz Habibi, Zachary Kingston, William Xie, Mathew Jellins, and James McLurkin

   In IEEE International Conference on Robotics and Automation

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   This paper presents four distributed motion controllers to enable a group of robots to collectively transport an object towards a guide robot. These controllers include: rotation around a pivot robot, rotation in-place around an estimated centroid of the object, translation, and a combined motion of rotation and translation in which each manipulating robot follows a trochoid path. Three of these controllers require an estimate of the centroid of the object, to use as the axis of rotation. Assuming the object is surrounded by manipulator robots, we approximate the centroid of the object by measuring the centroid of the manipulating robots. Our algorithms and controllers are fully distributed and robust to changes in network topology, robot population, and sensor error. We tested all of the algorithms in real-world environments with 9 robots, and show that the error of the centroid estimation is low, and that all four controllers produce reliable motion of the object.

   @inproceedings{habibi2015icra,
     author = {Habibi, Golnaz and Kingston, Zachary and Xie, William and Jellins, Mathew and McLurkin, James},
     title = {Distributed Centroid Estimation and Motion Controllers for Collective Transport by Multi-Robot Systems},
     booktitle = {IEEE International Conference on Robotics and Automation},
     year = {2015},
     pages = {1282--1288},
     doi = {10.1109/ICRA.2015.7139356},
   }