This paper presents a novel vehicle motion forecasting method based on multi-head
attention. It produces joint forecasts for all vehicles on a road scene as sequences of multi-modal …

In the recent vehicle trajectory prediction literature, the most common baselines are briefly
introduced without the necessary information to reproduce it. In this article we produce …

We study the design of learning architectures for behavioural planning in a dense traffic setting.
Such architectures should deal with a varying number of nearby vehicles, be invariant to …

Autonomous vehicle software is typically structured as a modular pipeline of individual
components (eg, perception, prediction, and planning) to help separate concerns into …

Scaling laws are useful guides for developing language models, but there are still gaps between
current scaling studies and how language models are ultimately trained and evaluated. …

The creation of large, diverse, high-quality robot manipulation datasets is an important
stepping stone on the path toward more capable and robust robotic manipulation policies. …

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-…

Imitation Learning (IL) is a widely used framework for learning imitative behavior from
demonstrations. It is especially appealing for solving complex real-world tasks where handcrafting …

Linear transformers have emerged as a subquadratic-time alternative to softmax attention
and have garnered significant interest due to their fixed-size recurrent state that lowers …

The ability to learn reward functions plays an important role in enabling the deployment of
intelligent agents in the real world. However, comparing reward functions, for example as a …