Carlo Bosio

• [Jul 2025] I am going to be at ACC 2025 in Denver presenting this paper on LLMs and control.
• [May 2025] I am going to spend the summer at Zoox in the Prediction and Behavior ML division.
• [Mar 2025] Matteo Guarrera and I are finalists for the 2025 Qualcomm Innovation Fellowship!
• [Feb 2025] My paper on the co-design of robust multi-agent aerial systems was accepted for publication on IEEE RA-L.
• [Jan 2025] My paper on synthesizing interpretable control policies with LLMs was accepted for presentation at ACC 2025.
• [Oct 2024] My preprint on synthesizing interpretable control policies with LLMs is available here!
• [Sept 2024] I am an organizer of BARS 2025!
• [Jun 2024] I am going to be part of the KTH RPL Summer School in Stockholm, Sweden!
• [Apr 2024] My work on ReachBot has been published on Science Robotics! Check it out here.
• [Oct 2023] I am going to be at IROS 2023 in Detroit presenting this RA-L paper I worked on during my undergrad.
• [Sep 2023] My preprint on Robust Multi-Agent Aerial Manipulation is available here!
• [Aug 2022] I started my PhD at Berkeley!

The combination of Large Language Models (LLMs), systematic evaluation, and evolutionary algorithms has enabled breakthroughs in combinatorial optimization and scientific discovery. We propose to extend this powerful combination to the control of dynamical systems, generating interpretable control policies capable of complex behaviors. With our novel method, we represent control policies as programs in standard languages like Python. We evaluate candidate controllers in simulation and evolve them using a pre-trained LLM. Unlike conventional learning-based control techniques, which rely on black box neural networks to encode control policies, our approach enhances transparency and interpretability. We still take advantage of the power of large AI models, but leverage it at the policy design phase, ensuring that all system components remain interpretable and easily verifiable at runtime. Additionally, the use of standard programming languages makes it straightforward for humans to finetune or adapt the controllers based on their expertise and intuition. We illustrate our method through its application to the synthesis of an interpretable control policy for the pendulum swing-up and the ball in cup tasks. We make the code available at https://github.com/muellerlab/synthesizing_interpretable_control_policies.git

@inproceedings{bosio2025synthesizing,
title={Synthesizing interpretable control policies through large language model guided search},
author={Bosio, Carlo and Mueller, Mark W},
booktitle={2025 American Control Conference (ACC)},
pages={583--590},
year={2025},
organization={IEEE}
}

The co-design of robotic systems, i.e. the joint optimization of physical parameters and controllers, is extremely challenging, due to the difficulties in predicting the effect that changes of physical parameters have on final performances. At the same time, physical and morphological modifications can drastically improve robot capabilities, perhaps completely unlocking new skills and tasks. We present a novel approach to co-optimize the physical layout and the control of a cooperative aerial transportation system. The goal is to achieve the most precise and robust flight when carrying a payload. We assume the agents are connected to the payload through rigid attachments, essentially transforming the whole system into a larger flying object with ''thrust modules'' at the attachment locations of the quadcopters. We investigate the optimal arrangement of the thrust modules around the payload, so that the resulting system achieves the best disturbance rejection capabilities. We choose the H2 norm as a metric of robustness, and propose an algorithm to optimize the layout of the vehicles around the object, and their control altogether. We experimentally validate the effectiveness and benefits of our approach using fleets of three and four quadcopters and payloads of diverse shapes.

@article{bosio2025automated,
title={Automated Layout and Control Co-Design of Robust Multi-UAV Transportation Systems},
author={Bosio, Carlo and Mueller, Mark W},
journal={IEEE Robotics and Automation Letters},
year={2025},
publisher={IEEE}
}

Caves and lava tubes on the Moon and Mars are sites of geological and astrobiological interest but consist of terrain that is inaccessible with traditional robot locomotion. To support the exploration of these sites, we present ReachBot, a robot that uses extendable booms as appendages to manipulate itself with respect to irregular rock surfaces. The booms terminate in grippers equipped with microspines and provide ReachBot with a large workspace, allowing it to achieve force closure in enclosed spaces, such as the walls of a lava tube. To propel ReachBot, we present a contact-before-motion planner for nongaited legged locomotion that uses internal force control, similar to a multifingered hand, to keep its long, slender booms in tension. Motion planning also depends on finding and executing secure grips on rock features. We used a Monte Carlo simulation to inform gripper design and predict grasp strength and variability. In addition, we used a two-step perception system to identify possible grasp locations. To validate our approach and mechanisms under realistic conditions, we deployed a single ReachBot arm and gripper in a lava tube in the Mojave Desert. The field test confirmed that ReachBot will find many targets for secure grasps with the proposed kinematic design.

@article{chen2024locomotion,
title={Locomotion as manipulation with ReachBot},
author={Chen, Tony G and Newdick, Stephanie and Di, Julia and Bosio, Carlo and Ongole, Nitin and Lap{\^o}tre, Mathieu and Pavone, Marco and Cutkosky, Mark R},
journal={Science Robotics},
volume={9},
number={89},
pages={eadi9762},
year={2024},
publisher={American Association for the Advancement of Science}
}