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Found 15 papers, 1 papers with code
Intent Demonstration in General-Sum Dynamic Games via Iterative Linear-Quadratic Approximations
Autonomous agents should be able to coordinate with other agents without knowing their intents ahead of time.
Adaptive Human Trajectory Prediction via Latent Corridors
We formalize the problem of scene-specific adaptive trajectory prediction and propose a new adaptation approach inspired by prompt tuning called latent corridors.
What Matters to You? Towards Visual Representation Alignment for Robot Learning
When operating in service of people, robots need to optimize rewards aligned with end-user preferences.
Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions
We introduce Conformal Decision Theory, a framework for producing safe autonomous decisions despite imperfect machine learning predictions.
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.
Learning Vision-based Pursuit-Evasion Robot Policies
We find that the quality of the supervision signal for the partially-observable pursuer policy depends on two key factors: the balance of diversity and optimality of the evader's behavior and the strength of the modeling assumptions in the fully-observable policy.
Towards Modeling and Influencing the Dynamics of Human Learning
Interestingly, robot actions influence what this experience is, and therefore influence how people's internal models change.
Towards Data-Driven Synthesis of Autonomous Vehicle Safety Concepts
As safety-critical autonomous vehicles (AVs) will soon become pervasive in our society, a number of safety concepts for trusted AV deployment have recently been proposed throughout industry and academia.
Physical Interaction as Communication: Learning Robot Objectives Online from Human Corrections
We recognize that physical human-robot interaction (pHRI) is often intentional -- the human intervenes on purpose because the robot is not doing the task correctly.
Analyzing Human Models that Adapt Online
This enables us to leverage tools from reachability analysis and optimal control to compute the set of hypotheses the robot could learn in finite time, as well as the worst and best-case time it takes to learn them.
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.
A Hamilton-Jacobi Reachability-Based Framework for Predicting and Analyzing Human Motion for Safe Planning
We construct a new continuous-time dynamical system, where the inputs are the observations of human behavior, and the dynamics include how the belief over the model parameters change.
An Efficient Reachability-Based Framework for Provably Safe Autonomous Navigation in Unknown Environments
Our safety method is planner-agnostic and provides guarantees for a variety of mapping sensors.
Robotics
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.
Probabilistically Safe Robot Planning with Confidence-Based Human Predictions
In order to safely operate around humans, robots can employ predictive models of human motion.
