Active simultaneous localization and map** (SLAM) is the problem of planning and
controlling the motion of a robot to build the most accurate and complete model of the …

Planning under uncertainty is critical to robotics. The partially observable Markov decision
process (POMDP) is a mathematical framework for such planning problems. POMDPs are …

This paper presents a comprehensive survey of Federated Reinforcement Learning (FRL),
an emerging and promising field in Reinforcement Learning (RL). Starting with a tutorial of …

A unifying mathematical formulation is needed to move from one-off digital twins built
through custom implementations to robust digital twin implementations at scale. This work …

We present foundations for using Model Predictive Control (MPC) as a differentiable policy
class for reinforcement learning. This provides one way of leveraging and combining the …

Many real-world sequential decision making problems are partially observable by nature,
and the environment model is typically unknown. Consequently, there is great need for …

Deep Learning (DL) has had an immense success in the recent past, leading to state-of-the-
art results in various domains, such as image recognition and natural language processing …

This article is about deep learning (DL) and deep reinforcement learning (DRL) works
applied to robotics. Both tools have been shown to be successful in delivering data-driven …

Autonomous vehicle (AV) stacks are typically built in a modular fashion, with explicit
components performing detection, tracking, prediction, planning, control, etc. While …

We present Neural A*, a novel data-driven search method for path planning problems.
Despite the recent increasing attention to data-driven path planning, machine learning …