Abstract Purpose of Review Current real-world interaction between humans and robots is
extremely limited. We present challenges that, if addressed, will enable humans and robots …

Imitation learning from a large set of human demonstrations has proved to be an effective
paradigm for building capable robot agents. However, the demonstrations can be extremely …

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

In supervised learning, the question of data quality and curation has been sidelined in
recent years in favor of increasingly more powerful and expressive models that can ingest …

Sim-to-real transfer is a powerful paradigm for robotic reinforcement learning. The ability to
train policies in simulation enables safe exploration and large-scale data collection quickly …

Many existing imitation learning datasets are collected from multiple demonstrators, each
with different expertise at different parts of the environment. Yet, standard imitation learning …

Reward functions are a common way to specify the objective of a robot. As designing reward
functions can be extremely challenging, a more promising approach is to directly learn …

Most existing imitation learning approaches assume the demonstrations are drawn from
experts who are optimal, but relaxing this assumption enables us to use a wider range of …

Preference-based reinforcement learning (PbRL) can enable robots to learn to perform tasks
based on an individual's preferences without requiring a hand-crafted re-ward function …

We present a deep-dive into a real-world robotic learning system that, in previous work, was
shown to be capable of hundreds of table tennis rallies with a human and has the ability to …