no code implementations • 21 Feb 2024 • Justin Lidard, Haimin Hu, Asher Hancock, Zixu Zhang, Albert Gimó Contreras, Vikash Modi, Jonathan DeCastro, Deepak Gopinath, Guy Rosman, Naomi Leonard, María Santos, Jaime Fernández Fisac
As intelligent robots like autonomous vehicles become increasingly deployed in the presence of people, the extent to which these systems should leverage model-based game-theoretic planners versus data-driven policies for safe, interaction-aware motion planning remains an open question.
no code implementations • 14 Feb 2024 • Haimin Hu, Gabriele Dragotto, Zixu Zhang, Kaiqu Liang, Bartolomeo Stellato, Jaime F. Fisac
To solve the problem, we introduce Branch and Play (B&P), an efficient and exact algorithm that provably converges to a socially optimal order of play and its Stackelberg equilibrium.
no code implementations • 11 Sep 2023 • Kai-Chieh Hsu, Haimin Hu, Jaime Fernández Fisac
Recent years have seen significant progress in the realm of robot autonomy, accompanied by the expanding reach of robotic technologies.
no code implementations • 3 Sep 2023 • Haimin Hu, Zixu Zhang, Kensuke Nakamura, Andrea Bajcsy, Jaime F. Fisac
An outstanding challenge for the widespread deployment of robotic systems like autonomous vehicles is ensuring safe interaction with humans without sacrificing performance.
1 code implementation • 5 Apr 2023 • Haimin Hu, Kensuke Nakamura, Kai-Chieh Hsu, Naomi Ehrich Leonard, Jaime Fernández Fisac
We present a multi-agent decision-making framework for the emergent coordination of autonomous agents whose intents are initially undecided.
1 code implementation • 1 Feb 2023 • Haimin Hu, David Isele, Sangjae Bae, Jaime F. Fisac
To ensure the safe operation of the interacting agents, we use a runtime safety filter (also referred to as a "shielding" scheme), which overrides the robot's dual control policy with a safety fallback strategy when a safety-critical event is imminent.
2 code implementations • 15 Feb 2022 • Haimin Hu, Jaime F. Fisac
The ability to accurately predict human behavior is central to the safety and efficiency of robot autonomy in interactive settings.
1 code implementation • 2 Oct 2021 • Haimin Hu, Kensuke Nakamura, Jaime F. Fisac
Leveraging recent work on Bayesian human motion prediction, the resulting robot policy proactively balances nominal performance with the risk of high-cost emergency maneuvers triggered by low-probability human behaviors.
no code implementations • 8 Nov 2020 • Lars Lindemann, Haimin Hu, Alexander Robey, Hanwen Zhang, Dimos V. Dimarogonas, Stephen Tu, Nikolai Matni
Motivated by the lack of systematic tools to obtain safe control laws for hybrid systems, we propose an optimization-based framework for learning certifiably safe control laws from data.
1 code implementation • 16 Apr 2020 • Haimin Hu, Mahyar Fazlyab, Manfred Morari, George J. Pappas
There has been an increasing interest in using neural networks in closed-loop control systems to improve performance and reduce computational costs for on-line implementation.
1 code implementation • 7 Apr 2020 • Alexander Robey, Haimin Hu, Lars Lindemann, Hanwen Zhang, Dimos V. Dimarogonas, Stephen Tu, Nikolai Matni
Furthermore, if the CBF parameterization is convex, then under mild assumptions, so is our learning process.