Robot Demos

                    @inproceedings{lee2024towards,
  title={Towards Autonomous Crop Monitoring: Inserting Sensors in Cluttered Environments},
  author={Lee, Moonyoung and Berger, Aaron and Guri, Dominic and Zhang, Kevin and Coffey, Lisa and Kantor, George and Kroemer, Oliver},
  journal={IEEE Robotics and Automation Letters},
  year={2024},
  publisher={IEEE}
}

                

Monitoring crop nutrients can aid farmers in optimizing fertilizer use. Many existing robots rely on vision-based phenotyping, however, which can only indirectly estimate nutrient deficiencies once crops have undergone visible color changes. We present a contact-based phenotyping robot platform that can directly insert nitrate sensors into cornstalks to proactively monitor macronutrient levels in crops. This task is challenging because inserting such sensors requires sub-centimeter precision in an environment which contains high levels of clutter, lighting variation, and occlusion. To address these challenges, we develop a robust perception-action pipeline to grasp stalks, and create a custom robot gripper which mechanically aligns the sensor before inserting it into the stalk. Through experimental validation on 48 unique stalks in a cornfield in Iowa, we demonstrate our platform's capability of detecting a stalk with 94% success.

                    @inproceedings{kim2024towards,
  title={Towards robotic tree manipulation: Leveraging graph representations},
  author={Kim, Chung Hee and Lee, Moonyoung and Kroemer, Oliver and Kantor, George},
  booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)},
  pages={11884--11890},
  year={2024},
  organization={IEEE}
}

                

There is growing interest in automating agricultural tasks that require intricate and precise interaction with specialty crops, such as trees and vines. However, developing robotic solutions for crop manipulation remains a difficult challenge due to complexities involved in modeling their deformable behavior. In this study, we present a framework for learning the deformation behavior of tree-like crops under contact interaction. Our proposed method involves encoding the state of a spring-damper modeled tree crop as a graph. This representation allows us to employ graph networks to learn both a forward model for predicting resulting deformations, and a contact policy for inferring actions to manipulate tree crops. We conduct a comprehensive set of experiments in a simulated environment and demonstrate generalizability of our method on previously unseen trees.

                    @ARTICLE{cheng2024hidex,
  author={Cheng, Xianyi and Patil, Sarvesh and Temel, Zeynep and Kroemer, Oliver and Mason, Matthew T.},
  journal={IEEE Robotics and Automation Letters}, 
  title={Enhancing Dexterity in Robotic Manipulation via Hierarchical Contact Exploration}, 
  year={2024},
  volume={9},
  number={1},
  pages={390-397},
  doi={10.1109/LRA.2023.3333699}}

                

Planning robot dexterity is challenging due to the non-smoothness introduced by contacts, intricate fine motions, and ever-changing scenarios. We present a hierarchical planning framework for dexterous robotic manipulation (HiDex). This framework explores in-hand and extrinsic dexterity by leveraging contacts. It generates rigid-body motions and complex contact sequences. Our framework is based on Monte-Carlo Tree Search and has three levels: 1) planning object motions and environment contact modes; 2) planning robot contacts; 3) path evaluation and control optimization. This framework offers two main advantages. First, it allows efficient global reasoning over high-dimensional complex space created by contacts. It solves a diverse set of manipulation tasks that require dexterity, both intrinsic (using the fingers) and extrinsic (also using the environment), mostly in seconds. Second, our framework allows the incorporation of expert knowledge and customizable setups in task mechanics and models. It requires minor modifications to accommodate different scenarios and robots. Hence, it provides a flexible and generalizable solution for various manipulation tasks. As examples, we analyze the results on 7 hand configurations and 15 scenarios. We demonstrate 8 tasks on two robot platforms.

                    @inproceedings{Patil_2023,
  title={Linear Delta Arrays for Compliant Dexterous Distributed Manipulation},
  url={http://dx.doi.org/10.1109/ICRA48891.2023.10160578},
  DOI={10.1109/icra48891.2023.10160578},
  booktitle={2023 IEEE International Conference on Robotics and Automation (ICRA)},
  publisher={IEEE},
  author={Patil, Sarvesh and Tao, Tony and Hellebrekers, Tess and Kroemer, Oliver and Temel, F. Zeynep},
  year={2023},
  month=may}

                

This paper presents a new type of distributed dexterous manipulator: delta arrays. Our delta array setup consists of 64 linearly-actuated delta robots with 3D-printed compliant linkages. Through the design of the individual delta robots, the modular array structure, and distributed communication and control, we study a wide range of in-plane and out-of-plane manipulations, as well as prehensile manipulations among subsets of neighboring delta robots. We also demonstrate dexterous manipulation capabilities of the delta array using reinforcement learning while leveraging the compliance to not break the end-effectors. Our evaluations show that the resulting 192 DoF compliant robot is capable of performing various coordinated distributed manipulations of a variety of objects, including translation, alignment, prehensile squeezing, lifting, and grasping.

                    @inproceedings{Patil_2022,
  title={DeltaZ: An Accessible Compliant Delta Robot Manipulator for Research and Education},
  volume={22},
  url={http://dx.doi.org/10.1109/IROS47612.2022.9981257},
  DOI={10.1109/iros47612.2022.9981257},
  booktitle={2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  publisher={IEEE},
  author={Patil, Sarvesh and Alvares, Samuel C. and Mannam, Pragna and Kroemer, Oliver and Temel, F. Zeynep},
  year={2022},
  month=oct, pages={13213–13219} }

                

This paper presents the DeltaZ robot, a centimeter-scale, low-cost, delta-style robot that allows for a broad range of capabilities and robust functionalities. Current technologies allow DeltaZ to be 3D-printed from soft and rigid materials so that it is easy to assemble and maintain, and lowers the barriers to utilize. Functionality of the robot stems from its three translational degrees of freedom and a closed form kinematic solution which makes manipulation problems more intuitive compared to other manipulators. Moreover, the low cost of the robot presents an opportunity to democratize manipulators for a research setting. We also describe how the robot can be used as a reinforcement learning benchmark. Open-source 3D-printable designs and code are available to the public.