We rethink the traditional reinforcement learning approach, which is based on optimizing
over feedback policies, and propose a new framework that optimizes over feedforward …

In this paper, we present a method for table tennis ball trajectory filtering and prediction. Our
gray-box approach builds on a physical model. At the same time, we use data to learn …

We present an implementation of an online op-timization algorithm for hitting a predefined
target when returning ping-pong balls with a table tennis robot. The online algorithm …

To approach the level of advanced human players in table tennis with robots, generating
varied ball trajectories in a reproducible and controlled manner is essential. Current ball …

A long-lasting goal of robotics research is to operate robots safely, while achieving high
performance which often involves fast motions. Traditional motor-driven systems frequently …

Reinforcement learning has traditionally focused on learning state-dependent policies to
solve optimal control problems in a closed-loop fashion. In this work, we introduce the …

This article aims to solve the trajectory tracking task of the pneumatic musculoskeletal robot
within a model-based reinforcement learning framework. Considering the limited sensors …

Remote magnetic manipulation offers wireless control over magnetic objects, which has
important medical applications, such as targeted drug delivery and minimally invasive …

Operating robots precisely and at high speeds has been a long-standing goal of robotics
research. Balancing these competing demands is key to enabling the seamless …

End-to-end speech recognition system can realize the conversion from input audio to
character text without complicated data processing, and it is widely used in speech …