565 citations · 1.1k across the 35 of their papers we have counts for
18 papers · 1 filter
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…
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…
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…
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…
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 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…
Hierarchical Game-Theoretic Planning for Autonomous Vehicles
Jaime F. Fisac, Eli Bronstein, Elis Stefansson +3
The actions of an autonomous vehicle on the road affect and are affected by those of other drivers, whether overtaking, negotiating a merge, or avoiding an accident. This mutual de…
Probabilistically Safe Robot Planning with Confidence-Based Human Predictions
Jaime F. Fisac, Andrea Bajcsy, Sylvia L. Herbert +4
In order to safely operate around humans, robots can employ predictive models of human motion. Unfortunately, these models cannot capture the full complexity of human behavior and…
Generating Plans that Predict Themselves
Jaime F. Fisac, Chang Liu, Jessica B. Hamrick +4
Collaboration requires coordination, and we coordinate by anticipating our teammates' future actions and adapting to their plan. In some cases, our teammates' actions early on can…
Goal Inference Improves Objective and Perceived Performance in Human-Robot Collaboration
Chang Liu, Jessica B. Hamrick, Jaime F. Fisac +4
The study of human-robot interaction is fundamental to the design and use of robotics in real-world applications. Robots will need to predict and adapt to the actions of human coll…
A General Safety Framework for Learning-Based Control in Uncertain Robotic Systems
Jaime F. Fisac, Anayo K. Akametalu, Melanie N. Zeilinger +3
The proven efficacy of learning-based control schemes strongly motivates their application to robotic systems operating in the physical world. However, guaranteeing correct operati…
FaSTrack: a Modular Framework for Fast and Guaranteed Safe Motion Planning
Sylvia L. Herbert, Mo Chen, SooJean Han +3
Fast and safe navigation of dynamical systems through a priori unknown cluttered environments is vital to many applications of autonomous systems. However, trajectory planning for…