80 citations · 106 across the 8 of their papers we have counts for
9 papers
Deception Game: Closing the Safety-Learning Loop in Interactive Robot Autonomy
Haimin Hu, Zixu Zhang, Kensuke Nakamura +2
An outstanding challenge for the widespread deployment of robotic systems like autonomous vehicles is ensuring safe interaction with humans without sacrificing performance. Existin…
Emergent Coordination through Game-Induced Nonlinear Opinion Dynamics
Haimin Hu, Kensuke Nakamura, Kai-Chieh Hsu +2
We present a multi-agent decision-making framework for the emergent coordination of autonomous agents whose intents are initially undecided. Dynamic non-cooperative games have been…
Active Uncertainty Reduction for Safe and Efficient Interaction Planning: A Shielding-Aware Dual Control Approach
Haimin Hu, David Isele, Sangjae Bae +1
The ability to accurately predict others' behavior is central to the safety and efficiency of interactive robotics. Unfortunately, robots often lack access to key information on wh…
Active Uncertainty Reduction for Human-Robot Interaction: An Implicit Dual Control Approach
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. Unfortunately, robots often lack access to key i…
SHARP: Shielding-Aware Robust Planning for Safe and Efficient Human-Robot Interaction
Haimin Hu, Kensuke Nakamura, Jaime F. Fisac
Jointly achieving safety and efficiency in human-robot interaction (HRI) settings is a challenging problem, as the robot's planning objectives may be at odds with the human's own i…
FaSTrack: a Modular Framework for Real-Time Motion Planning and Guaranteed Safe Tracking
Mo Chen, Sylvia L. Herbert, Haimin Hu +5
Real-time, guaranteed safe trajectory planning is vital for navigation in unknown environments. However, real-time navigation algorithms typically sacrifice robustness for computat…
Learning Hybrid Control Barrier Functions from Data
Lars Lindemann, Haimin Hu, Alexander Robey +4
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…
Reach-SDP: Reachability Analysis of Closed-Loop Systems with Neural Network Controllers via Semidefinite Programming
Haimin Hu, Mahyar Fazlyab, Manfred Morari +1
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. Howe…
Learning Control Barrier Functions from Expert Demonstrations
Alexander Robey, Haimin Hu, Lars Lindemann +4
Inspired by the success of imitation and inverse reinforcement learning in replicating expert behavior through optimal control, we propose a learning based approach to safe control…