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 …

In this paper, we introduce physics-augmented autoencoder (PAA), a framework for 3D
skeleton-based human gait recognition. Specifically, we construct the autoencoder with a …

We study the choice of action space in robot manipulation learning and sim-to-real transfer.
We define metrics that assess the performance, and examine the emerging properties in the …

This report presents the debates, posters, and discussions of the Sim2Real workshop held
in conjunction with the 2020 edition of the" Robotics: Science and System" conference …

Differentiable physics has recently been shown as a powerful tool for solving soft-body
manipulation tasks. However, the differentiable physics solver often gets stuck when the …

Research on yield predictions is dominated by two approaches: machine learning and
process-based models. Machine learning has shown impressive results in capturing …

Learning policies in simulation is promising for reducing human effort when training robot
controllers. This is especially true for soft robots that are more adaptive and safe but also …

Efficient generalisation in supply chain inventory management is challenging due to a
potential mismatch between the model optimised and objective reality. It is hard to know how …

The hybrid nature of multi-contact robotic systems, due to making and breaking contact with
the environment, creates significant challenges for high-quality control. Existing model …

Achieving robust walking for different stairs is one of the most challenging tasks for
quadruped robots in real world. Traditional model-based methods heavily rely on …