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 …

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 …

So-called robot–object Lagrangian systems consist of a class of nonsmooth underactuated
complementarity Lagrangian systems, with a specific structure: an “object” and a “robot” …

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 …

Probabilistic representations of movement primitives open important new possibilities for
machine learning in robotics. These representations are able to capture the variability of the …

Achieving human-level speed and performance on real world tasks is a north star for the
robotics research community. This work takes a step towards that goal and presents the first …

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 …

One of the biggest challenges hindering a table tennis robot to play as well as a professional
player is the ball's accurate motion control, which depends on various factors such as the …

Sports robots have become a popular research topic in recent years. For table-tennis robots,
ball tracking and trajectory prediction are the most important technologies. Several methods …