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Found 6 papers, 0 papers with code
Decision-Making Among Bounded Rational Agents
Using both simulated and real-world experiments in multi-robot navigation tasks, we demonstrate that the resulting framework allows the robots to reason about different levels of rational behaviors of other agents and compute a reasonable strategy under its computational constraint.
Online Planning in Uncertain and Dynamic Environment in the Presence of Multiple Mobile Vehicles
We first predict the future state distributions of other vehicles to account for their uncertain behaviors affected by the time-varying disturbances.
Kernel Taylor-Based Value Function Approximation for Continuous-State Markov Decision Processes
We propose a principled kernel-based policy iteration algorithm to solve the continuous-state Markov Decision Processes (MDPs).
Reachable Space Characterization of Markov Decision Processes with Time Variability
We propose a solution to a time-varying variant of Markov Decision Processes which can be used to address decision-theoretic planning problems for autonomous systems operating in unstructured outdoor environments.
Shared Multi-Task Imitation Learning for Indoor Self-Navigation
Deep imitation learning enables robots to learn from expert demonstrations to perform tasks such as lane following or obstacle avoidance.
Avoidance of Manual Labeling in Robotic Autonomous Navigation Through Multi-Sensory Semi-Supervised Learning
This solution includes a suboptimal sensor policy based on sensor fusion to automatically label states encountered by a robot to avoid human supervision during training.
