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Found 11 papers, 6 papers with code
Blending Data-Driven Priors in Dynamic Games
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.
Who Plays First? Optimizing the Order of Play in Stackelberg Games with Many Robots
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.
The Safety Filter: A Unified View of Safety-Critical Control in Autonomous Systems
Recent years have seen significant progress in the realm of robot autonomy, accompanied by the expanding reach of robotic technologies.
Deception Game: Closing the Safety-Learning Loop in Interactive Robot Autonomy
An outstanding challenge for the widespread deployment of robotic systems like autonomous vehicles is ensuring safe interaction with humans without sacrificing performance.
Emergent Coordination through Game-Induced Nonlinear Opinion Dynamics
We present a multi-agent decision-making framework for the emergent coordination of autonomous agents whose intents are initially undecided.
Active Uncertainty Reduction for Safe and Efficient Interaction Planning: A Shielding-Aware Dual Control Approach
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.
Active Uncertainty Reduction for Human-Robot Interaction: An Implicit Dual Control Approach
The ability to accurately predict human behavior is central to the safety and efficiency of robot autonomy in interactive settings.
SHARP: Shielding-Aware Robust Planning for Safe and Efficient Human-Robot Interaction
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.
Learning Hybrid Control Barrier Functions from Data
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.
Reach-SDP: Reachability Analysis of Closed-Loop Systems with Neural Network Controllers via Semidefinite Programming
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.
Learning Control Barrier Functions from Expert Demonstrations
Furthermore, if the CBF parameterization is convex, then under mild assumptions, so is our learning process.
