While reinforcement learning (RL) has become a more popular approach for robotics,
designing sufficiently informative reward functions for complex tasks has proven to be …

Reward functions are difficult to design and often hard to align with human intent. Preference-
based Reinforcement Learning (RL) algorithms address these problems by learning reward …

There is a recent trend of applying multi-agent reinforcement learning (MARL) to train an
agent that can cooperate with humans in a zero-shot fashion without using any human data …

Robust planning in interactive scenarios requires predicting the uncertain future to make risk-
aware decisions. Unfortunately, due to long-tail safety-critical events, the risk is often under …

We propose a new framework for imitation learning--treating imitation as a two-player
ranking-based game between a policy and a reward. In this game, the reward agent learns …

Abstract Agent-Based Models (ABM) are computational models that utilize autonomous
agents to interact and adapt to the environments in which they occupy. They are used in …

As robots become more integrated into society, their reasoning and actions will invariably be
evaluated by human decision makers. Thus, robots need to perceive, act, and reason like …