The construction industry is seeking a robotic revolution to meet increasing demands for
productivity, quality, and safety. Typically, construction robots are usually pre-programmed …

Reinforcement learning can acquire complex behaviors from high-level specifications.
However, defining a cost function that can be optimized effectively and encodes the correct …

Learning from demonstration, or imitation learning, is the process of learning to act in an
environment from examples provided by a teacher. Inverse reinforcement learning (IRL) is a …

Optimizing behaviors for dexterous manipulation has been a longstanding challenge in
robotics, with a variety of methods from model-based control to model-free reinforcement …

Abstract Learning from Demonstration (LfD) seeks to democratize robotics by enabling non-
roboticist end-users to teach robots to perform a task by providing a human demonstration …

Visual imitation learning provides a framework for learning complex manipulation behaviors
by leveraging human demonstrations. However, current interfaces for imitation such as …

This article proposes a data-driven inverse reinforcement learning (RL) control algorithm for
nonzero-sum multiplayer games in linear continuous-time differential dynamical systems …

Dynamic tasks such as table tennis are relatively easy to learn for humans, but pose
significant challenges to robots. Such tasks require accurate control of fast movements and …

We study the problem of modeling spatiotemporal trajectories over long time horizons using
expert demonstrations. For instance, in sports, agents often choose action sequences with …

Model-free episodic reinforcement learning problems define the environment reward with
functions that often provide only sparse information throughout the task. Consequently …