As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented
attention because of its promise to further optimize process design, quality control, health …

The use of simulators in robotics research is widespread, underpinning the majority of recent
advances in the field. There are now more options available to researchers than ever before …

For a robot to personalize physical assistance effectively, it must learn user preferences that
can be generally reapplied to future scenarios. In this work, we investigate personalization of …

Transformers have revolutionized vision and natural language processing with their ability to
scale with large datasets. But in robotic manipulation, data is both limited and expensive …

We find that across a wide range of robot policy learning scenarios, treating supervised
policy learning with an implicit model generally performs better, on average, than commonly …

In offline reinforcement learning (RL), the goal is to learn a highly rewarding policy based
solely on a dataset of historical interactions with the environment. This serves as an extreme …

Abstract Purpose of Review This review provides a comprehensive overview of machine
learning approaches for vision-based robotic grasping and manipulation. Current trends and …

Building home assistant robots has long been a goal for vision and robotics researchers. To
achieve this task, a simulated environment with physically realistic simulation, sufficient …

Robot learning holds the promise of learning policies that generalize broadly. However,
such generalization requires sufficiently diverse datasets of the task of interest, which can be …

Rearranging and manipulating deformable objects such as cables, fabrics, and bags is a
long-standing challenge in robotic manipulation. The complex dynamics and high …