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Found 21 papers, 8 papers with code
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.
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.
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.
Sim-to-Lab-to-Real: Safe Reinforcement Learning with Shielding and Generalization Guarantees
To improve safety, we apply a dual policy setup where a performance policy is trained using the cumulative task reward and a backup (safety) policy is trained by solving the Safety Bellman Equation based on Hamilton-Jacobi (HJ) reachability analysis.
Safety and Liveness Guarantees through Reach-Avoid Reinforcement Learning
Recent successes in reinforcement learning methods to approximately solve optimal control problems with performance objectives make their application to certification problems attractive; however, the Lagrange-type objective used in reinforcement learning is not suitable to encode temporal logic requirements.
ProBF: Learning Probabilistic Safety Certificates with Barrier Functions
In practice, we have inaccurate knowledge of the system dynamics, which can lead to unsafe behaviors due to unmodeled residual dynamics.
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.
Back to the Future: Efficient, Time-Consistent Solutions in Reach-Avoid Games
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 criterion.
Quantifying Hypothesis Space Misspecification in Learning from Human-Robot Demonstrations and Physical Corrections
Recent work focuses on how robots can use such input - like demonstrations or corrections - to learn intended objectives.
LESS is More: Rethinking Probabilistic Models of Human Behavior
A common model is the Boltzmann noisily-rational decision model, which assumes people approximately optimize a reward function and choose trajectories in proportion to their exponentiated reward.
Safely Probabilistically Complete Real-Time Planning and Exploration in Unknown Environments
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 environments.
Robotics Systems and Control
Hierarchical Game-Theoretic Planning for Autonomous Vehicles
This mutual dependence, best captured by dynamic game theory, creates a strong coupling between the vehicle's planning and its predictions of other drivers' behavior, and constitutes an open problem with direct implications on the safety and viability of autonomous driving technology.
Learning under Misspecified Objective Spaces
Learning robot objective functions from human input has become increasingly important, but state-of-the-art techniques assume that the human's desired objective lies within the robot's hypothesis space.
An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning
We apply this update to a variety of POMDP solvers and find that it enables us to scale CIRL to non-trivial problems, with larger reward parameter spaces, and larger action spaces for both robot and human.
Probabilistically Safe Robot Planning with Confidence-Based Human Predictions
In order to safely operate around humans, robots can employ predictive models of human motion.
Generating Plans that Predict Themselves
We introduce $t$-\ACty{}: a measure that quantifies the accuracy and confidence with which human observers can predict the remaining robot plan from the overall task goal and the observed initial $t$ actions in the plan.
Goal Inference Improves Objective and Perceived Performance in Human-Robot Collaboration
The study of human-robot interaction is fundamental to the design and use of robotics in real-world applications.
Planning, Fast and Slow: A Framework for Adaptive Real-Time Safe Trajectory Planning
Motion planning is an extremely well-studied problem in the robotics community, yet existing work largely falls into one of two categories: computationally efficient but with few if any safety guarantees, or able to give stronger guarantees but at high computational cost.
Systems and Control Computer Science and Game Theory
Pragmatic-Pedagogic Value Alignment
In robotics, value alignment is key to the design of collaborative robots that can integrate into human workflows, successfully inferring and adapting to their users' objectives as they go.
FaSTrack: a Modular Framework for Fast and Guaranteed Safe Motion Planning
We propose a new algorithm FaSTrack: Fast and Safe Tracking for High Dimensional systems.
Robotics
