In recent years, the development of robotics and artificial intelligence (AI) systems has been
nothing short of remarkable. As these systems continue to evolve, they are being utilized in …

Reinforcement learning is a learning paradigm for solving sequential decision-making
problems. Recent years have witnessed remarkable progress in reinforcement learning …

Simulation has the potential to massively scale evaluation of self-driving systems, enabling
rapid development as well as safe deployment. Bridging the gap between simulation and …

Reinforcement learning from human feedback (RLHF) is a standard approach for fine-tuning
large language models to follow instructions. As part of this process, learned reward models …

Learning socially-aware motion representations is at the core of recent advances in multi-
agent problems, such as human motion forecasting and robot navigation in crowds. Despite …

This paper studies offline Imitation Learning (IL) where an agent learns to imitate an expert
demonstrator without additional online environment interactions. Instead, the learner is …

Imitation Learning (IL) methods seek to match the behavior of an agent with that of an expert.
In the present work, we propose a new IL method based on a conceptually simple algorithm …

Imitation learning is well-suited for robotic tasks where it is difficult to directly program the
behavior or specify a cost for optimal control. In this work, we propose a method for learning …

Measuring and promoting policy diversity is critical for solving games with strong non-
transitive dynamics where strategic cycles exist, and there is no consistent winner (eg, Rock …

Learning-based approaches, such as reinforcement learning (RL) and imitation learning
(IL), have indicated superiority over rule-based approaches in complex urban autonomous …