Soft robots have garnered interest for real-world applications because of their intrinsic safety
embedded at the material level. These robots use deformable materials capable of shape …

Deep reinforcement learning (RL) has become one of the most popular topics in artificial
intelligence research. It has been widely used in various fields, such as end-to-end control …

Large transformer models trained on diverse datasets have shown a remarkable ability to
learn in-context, achieving high few-shot performance on tasks they were not explicitly …

Abstract Model-based reinforcement learning (RL) algorithms can attain excellent sample
efficiency, but often lag behind the best model-free algorithms in terms of asymptotic …

Conventionally, model-based reinforcement learning (MBRL) aims to learn a global model
for the dynamics of the environment. A good model can potentially enable planning …

Large transformer models pretrained on offline reinforcement learning datasets have
demonstrated remarkable in-context reinforcement learning (ICRL) capabilities, where they …

Although reinforcement learning methods can achieve impressive results in simulation, the
real world presents two major challenges: generating samples is exceedingly expensive …

Contact-rich manipulation tasks in unstructured environments often require both haptic and
visual feedback. However, it is non-trivial to manually design a robot controller that …

Model-free reinforcement learning (RL) methods are succeeding in a growing number of
tasks, aided by recent advances in deep learning. However, they tend to suffer from high …

A key challenge in intelligent robotics is creating robots that are capable of directly
interacting with the world around them to achieve their goals. The last decade has seen …