565 citations · 1.1k across the 35 of their papers we have counts for
41 papers
An Update to the Level Set Theorems in Hamilton-Jacobi Reachability Analysis
Dylan Hirsch, William McEneaney, Jaime Fisac +2
Hamilton-Jacobi Reachability (HJR) is an important framework for controlling safety-critical systems despite uncertainty. Its theoretical underpinnings are rooted in Hamilton-Jacob…
Human-AI Safety: A Descendant of Generative AI and Control Systems Safety
Andrea Bajcsy, Jaime F. Fisac
Artificial intelligence (AI) is interacting with people at an unprecedented scale, offering new avenues for immense positive impact, but also raising widespread concerns around the…
Gameplay Filters: Robust Zero-Shot Safety through Adversarial Imagination
Duy P. Nguyen, Kai-Chieh Hsu, Wenhao Yu +2
Despite the impressive recent advances in learning-based robot control, ensuring robustness to out-of-distribution conditions remains an open challenge. Safety filters can, in prin…
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…
The Safety Filter: A Unified View of Safety-Critical Control in Autonomous Systems
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. However, the emergence of new deployment dom…
Fast, Smooth, and Safe: Implicit Control Barrier Functions through Reach-Avoid Differential Dynamic Programming
Athindran Ramesh Kumar, Kai-Chieh Hsu, Peter J. Ramadge +1
Safety is a central requirement for autonomous system operation across domains. Hamilton-Jacobi (HJ) reachability analysis can be used to construct "least-restrictive" safety filte…
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…
Learning Rationality in Potential Games
Stefan Clarke, Gabriele Dragotto, Jaime Fernández Fisac +1
We propose a stochastic first-order algorithm to learn the rationality parameters of simultaneous and non-cooperative potential games, i.e., the parameters of the agents' optimizat…
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…
ISAACS: Iterative Soft Adversarial Actor-Critic for Safety
Kai-Chieh Hsu, Duy Phuong Nguyen, Jaime Fernández Fisac
The deployment of robots in uncontrolled environments requires them to operate robustly under previously unseen scenarios, like irregular terrain and wind conditions. Unfortunately…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
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…
Sim-to-Lab-to-Real: Safe Reinforcement Learning with Shielding and Generalization Guarantees
Kai-Chieh Hsu, Allen Z. Ren, Duy Phuong Nguyen +2
Safety is a critical component of autonomous systems and remains a challenge for learning-based policies to be utilized in the real world. In particular, policies learned using rei…
ProBF: Learning Probabilistic Safety Certificates with Barrier Functions
Athindran Ramesh Kumar, Sulin Liu, Jaime F. Fisac +2
Safety-critical applications require controllers/policies that can guarantee safety with high confidence. The control barrier function is a useful tool to guarantee safety if we ha…
Safety and Liveness Guarantees through Reach-Avoid Reinforcement Learning
Kai-Chieh Hsu, Vicenç Rubies-Royo, Claire J. Tomlin +1
Reach-avoid optimal control problems, in which the system must reach certain goal conditions while staying clear of unacceptable failure modes, are central to safety and liveness a…
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…
Back to the Future: Efficient, Time-Consistent Solutions in Reach-Avoid Games
Dennis R. Anthony, Duy P. Nguyen, David Fridovich-Keil +1
We study the class of reach-avoid dynamic games in which multiple agents interact noncooperatively, and each wishes to satisfy a distinct target criterion while avoiding a failure…
Safe Occlusion-aware Autonomous Driving via Game-Theoretic Active Perception
Zixu Zhang, Jaime F. Fisac
Autonomous vehicles interacting with other traffic participants heavily rely on the perception and prediction of other agents' behaviors to plan safe trajectories. However, as occl…
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…
Quantifying Hypothesis Space Misspecification in Learning from Human-Robot Demonstrations and Physical Corrections
Andreea Bobu, Andrea Bajcsy, Jaime F. Fisac +2
Human input has enabled autonomous systems to improve their capabilities and achieve complex behaviors that are otherwise challenging to generate automatically. Recent work focuses…
LESS is More: Rethinking Probabilistic Models of Human Behavior
Andreea Bobu, Dexter R. R. Scobee, Jaime F. Fisac +2
Robots need models of human behavior for both inferring human goals and preferences, and predicting what people will do. A common model is the Boltzmann noisily-rational decision m…
A Risk-Sensitive Finite-Time Reachability Approach for Safety of Stochastic Dynamic Systems
Margaret P. Chapman, Jonathan Lacotte, Aviv Tamar +7
A classic reachability problem for safety of dynamic systems is to compute the set of initial states from which the state trajectory is guaranteed to stay inside a given constraint…
A Scalable Framework For Real-Time Multi-Robot, Multi-Human Collision Avoidance
Andrea Bajcsy, Sylvia L. Herbert, David Fridovich-Keil +4
Robust motion planning is a well-studied problem in the robotics literature, yet current algorithms struggle to operate scalably and safely in the presence of other moving agents,…
Safely Probabilistically Complete Real-Time Planning and Exploration in Unknown Environments
David Fridovich-Keil, Jaime F. Fisac, Claire J. Tomlin
We present a new framework for motion planning that wraps around existing kinodynamic planners and guarantees recursive feasibility when operating in a priori unknown, static envir…