publications

2025

1. 

   Push Anything: Single- and Multi-Object Pushing From First Sight with Contact-Implicit MPC

   Hien Bui^*, Yufeiyang Gao^*, Haoran Yang^*, Eric Cui, Siddhant Mody, Brian Acosta, Thomas Stephen Felix, Bibit Bianchini, and Michael Posa

   Under Review, Oct 2025

   Abs DOI Bib PDF Website

   

   Non-prehensile manipulation of diverse objects remains a core challenge in robotics, driven by unknown physical properties and the complexity of contact-rich interactions. Recent advances in contact-implicit model predictive control (CI-MPC), with contact reasoning embedded directly in the trajectory optimization, have shown promise in tackling the task efficiently and robustly, yet demonstrations have been limited to narrowly curated examples. In this work, we showcase the broader capabilities of CI-MPC through precise planar pushing tasks over a wide range of object geometries, including multi-object domains. These scenarios demand reasoning over numerous inter-object and object-environment contacts to strategically manipulate and de-clutter the environment, challenges that were intractable for prior CI-MPC methods. To achieve this, we introduce Consensus Complementarity Control Plus (C3+), an enhanced CI-MPC algorithm integrated into a complete pipeline spanning object scanning, mesh reconstruction, and hardware execution. Compared to its predecessor C3, C3+ achieves substantially faster solve times, enabling real-time performance even in multi-object pushing tasks. On hardware, our system achieves overall 98% success rate across 33 objects, reaching pose goals within tight tolerances. The average time-to-goal is approximately 0.5, 1.6, 3.2, and 5.3 minutes for 1-, 2-, 3-, and 4-object tasks, respectively.

   @article{Bui2026,
     title = {Push Anything: Single- and Multi-Object Pushing From First Sight with Contact-Implicit MPC},
     author = {Bui, Hien and Gao, Yufeiyang and Yang, Haoran and Cui, Eric and Mody, Siddhant and Acosta, Brian and Felix, Thomas Stephen and Bianchini, Bibit and Posa, Michael},
     journal = {Under Review},
     year = {2025},
     month = oct,
     doi = {10.48550/arXiv.2510.19974},
     url = {https://arxiv.org/abs/2510.19974},
   }

2. 

   Active Tactile Exploration for Rigid Body Pose and Shape Estimation

   Ethan K. Gordon, Hien Bui, and Michael Posa

   Under Review, Oct 2025

   Abs DOI Bib PDF Website

   

   General robot manipulation requires the handling of previously unseen objects. Learning a physically accurate model at test time can provide significant benefits in data efficiency, predictability, and reuse between tasks. Tactile sensing can compliment vision with its robustness to occlusion, but its temporal sparsity necessitates careful online exploration to maintain data efficiency. Direct contact can also cause an unrestrained object to move, requiring both shape and location estimation. In this work, we propose a learning and exploration framework that uses only tactile data to simultaneously determine the shape and location of rigid objects with minimal robot motion. We build on recent advances in contact-rich system identification to formulate a loss function that penalizes physical constraint violation without introducing the numerical stiffness inherent in rigid-body contact. Optimizing this loss, we can learn cuboid and convex polyhedral geometries with less than 10s of randomly collected data after first contact. Our exploration scheme seeks to maximize Expected Information Gain and results in significantly faster learning in both simulated and real-robot experiments. More information can be found at https://dairlab.github.io/activetactile

   @article{gordon2026active,
     title = {Active Tactile Exploration for Rigid Body Pose and Shape Estimation},
     author = {Gordon, Ethan K. and Bui, Hien and Posa, Michael},
     journal = {Under Review},
     year = {2025},
     month = oct,
     doi = {10.48550/arXiv.2510.13595},
     url = {https://arxiv.org/abs/2510.13595},
   }

2024

1. 

   Enhancing Task Performance of Learned Simplified Models via Reinforcement Learning

   Hien Bui and Michael Posa

   In IEEE International Conference on Robotics and Automation (ICRA), Yokohama, Japan, May 2024

   Abs DOI Bib PDF Website

   

   In contact-rich tasks, the hybrid, multi-modal nature of contact dynamics poses great challenges in model representation, planning, and control. Recent efforts have attempted to address these challenges via data-driven methods, learning dynamical models in combination with model predictive control. Those methods, while effective, rely solely on minimizing forward prediction errors to hope for better task performance with MPC controllers. This weak correlation can result in data inefficiency as well as limitations to overall performance. In response, we propose a novel strategy: using a policy gradient algorithm to find a simplified dynamics model that explicitly maximizes task performance. Specifically, we parameterize the stochastic policy as the perturbed output of the MPC controller, thus, the learned model representation can directly associate with the policy or task performance. We apply the proposed method to contact-rich tasks where a three-fingered robotic hand manipulates previously unknown objects. Our method significantly enhances task success rate by up to 15% in manipulating diverse objects compared to the existing method while sustaining data efficiency. Our method can solve some tasks with success rates of 70% or higher using under 30 minutes of data. All videos and codes are available at https://sites.google.com/view/lcs-rl.

   @inproceedings{10611461,
     author = {Bui, Hien and Posa, Michael},
     booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
     title = {Enhancing Task Performance of Learned Simplified Models via Reinforcement Learning},
     year = {2024},
     month = may,
     volume = {},
     number = {},
     pages = {9212-9219},
     keywords = {System dynamics;Heuristic algorithms;Transfer learning;Stochastic processes;Reinforcement learning;Predictive models;Task analysis},
     location = {Yokohama, Japan},
     doi = {10.1109/ICRA57147.2024.10611461},
   }

2. 

   Reinforcement learning for reduced-order models of legged robots

   Yu-Ming Chen, Hien Bui, and Michael Posa

   In IEEE International Conference on Robotics and Automation (ICRA), Yokohama, Japan, May 2024

   Abs DOI Bib PDF Website

   

   Model-based approaches for planning and control for bipedal locomotion have a long history of success. It can provide stability and safety guarantees while being effective in accomplishing many locomotion tasks. Model-free reinforcement learning, on the other hand, has gained much popularity in recent years due to computational advancements. It can achieve high performance in specific tasks, but it lacks physical interpretability and flexibility in re-purposing the policy for a different set of tasks. For instance, we can initially train a neural network (NN) policy using velocity commands as inputs. However, to handle new task commands like desired hand or footstep locations at a desired walking velocity, we must retrain a new NN policy. In this work, we attempt to bridge the gap between these two bodies of work on a bipedal platform. We formulate a model-based reinforcement learning problem to learn a reduced-order model (ROM) within a model predictive control (MPC). Results show a 49% improvement in viable task region size and a 21% reduction in motor torque cost. All videos and code are available at https://sites.google.com/view/ymchen/research/rl-for-roms.

   @inproceedings{10610747,
     author = {Chen, Yu-Ming and Bui, Hien and Posa, Michael},
     booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
     title = {Reinforcement learning for reduced-order models of legged robots},
     year = {2024},
     month = may,
     volume = {},
     number = {},
     pages = {5801-5807},
     keywords = {Legged locomotion;Torque;Computational modeling;Reinforcement learning;Artificial neural networks;Reduced order systems;Stability analysis},
     location = {Yokohama, Japan},
     doi = {10.1109/ICRA57147.2024.10610747},
   }

2018

1. 

   An ultralightweight and living legged robot

   Tat Thang Vo Doan, Melvin Y.W. Tan^*, Hien Bui^*, and Hirotaka Sata

   Soft Robotics, Feb 2018

   PMID: 29412086

   Abs DOI Bib PDF

   

   In this study, we describe the most ultralightweight living legged robot to date that makes it a strong candidate for a search and rescue mission. The robot is a living beetle with a wireless electronic backpack stimulator mounted on its thorax. Inheriting from the living insect, the robot employs a compliant body made of soft actuators, rigid exoskeletons, and flexure hinges. Such structure would allow the robot to easily adapt to any complex terrain due to the benefit of soft interface, self-balance, and self-adaptation of the insect without any complex controller. The antenna stimulation enables the robot to perform not only left/right turning but also backward walking and even cessation of walking. We were also able to grade the turning and backward walking speeds by changing the stimulation frequency. The power required to drive the robot is low as the power consumption of the antenna stimulation is in the order of hundreds of microwatts. In contrast to the traditional legged robots, this robot is of low cost, easy to construct, simple to control, and has ultralow power consumption.

   @article{doi:10.1089/soro.2017.0038,
     title = {An ultralightweight and living legged robot},
     author = {Vo Doan, Tat Thang and Tan, Melvin Y.W. and Bui, Hien and Sata, Hirotaka},
     journal = {Soft Robotics},
     volume = {5},
     number = {1},
     pages = {17-23},
     year = {2018},
     month = feb,
     doi = {10.1089/soro.2017.0038},
     note = {PMID: 29412086},
     url = {https://www.liebertpub.com/doi/abs/10.1089/soro.2017.0038},
     eprint = {https://doi.org/10.1089/soro.2017.0038},
   }