Search Results for author:
Found 5 papers, 2 papers with code
Generating and Explaining Corner Cases Using Learnt Probabilistic Lane Graphs
We use reinforcement learning techniques to modify this policy and to generate realistic and explainable corner case scenarios which can be used for assessing the safety of AVs.
Towards Probabilistic Causal Discovery, Inference & Explanations for Autonomous Drones in Mine Surveying Tasks
Here we identify challenges relating to causality in the context of a drone system operating in a salt mine.
Simulation-Based Counterfactual Causal Discovery on Real World Driver Behaviour
Being able to reason about how one's behaviour can affect the behaviour of others is a core skill required of intelligent driving agents.
Evaluating Temporal Observation-Based Causal Discovery Techniques Applied to Road Driver Behaviour
Autonomous robots are required to reason about the behaviour of dynamic agents in their environment.
Learning Transferable Push Manipulation Skills in Novel Contexts
We propose to learn a parametric internal model for push interactions that, similar for humans, enables a robot to predict the outcome of a physical interaction even in novel contexts.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
