6 papers
Linking Behaviour and Perception to Evaluate Meaningful Human Control over Partially Automated Driving
Ashwin George, Lucas Elbert Suryana, Lorenzo Flipse +5
Partial driving automation creates a tension: drivers remain legally responsible while being less active in control. Meaningful human control (MHC), a normative framework that can…
A Behavior-first approach to Multi-Intention Inverse Reinforcement Learning
Antonio Mone, Frans A. Oliehoek, Luciano Cavalcante Siebert
Inverse Reinforcement Learning (IRL) seeks to infer reward functions from expert demonstrations. When demonstrations originate from multiple experts with different intentions, the…
Using Feasible Action-Space Reduction by Groups to fill Causal Responsibility Gaps in Spatial Interactions
Ashwin George, Vassil Guenov, Arkady Zgonnikov +2
Heralding the advent of autonomous vehicles and mobile robots that interact with humans, responsibility in spatial interaction is burgeoning as a research topic. Even though metric…
Feasible Action Space Reduction for Quantifying Causal Responsibility in Continuous Spatial Interactions
Ashwin George, Luciano Cavalcante Siebert, David A. Abbink +1
Understanding the causal influence of one agent on another agent is crucial for safely deploying artificially intelligent systems such as automated vehicles and mobile robots into…
ARMCHAIR: integrated inverse reinforcement learning and model predictive control for human-robot collaboration
Angelo Caregnato-Neto, Luciano Cavalcante Siebert, Arkady Zgonnikov +2
One of the key issues in human-robot collaboration is the development of computational models that allow robots to predict and adapt to human behavior. Much progress has been achie…
Explaining Learned Reward Functions with Counterfactual Trajectories
Jan Wehner, Frans Oliehoek, Luciano Cavalcante Siebert
Learning rewards from human behaviour or feedback is a promising approach to aligning AI systems with human values but fails to consistently extract correct reward functions. Inter…