Learning in simulation and transferring the learned policy to the real world has the potential
to enable generalist robots. The key challenge of this approach is to address simulation-to …

Designing robotic agents to perform open vocabulary tasks has been the long-standing goal
in robotics and AI. Recently, Large Language Models (LLMs) have achieved impressive …

Abstract Learning from Demonstration (LfD) is an efficient technique for robots to acquire
new skills through expert observation, significantly mitigating the need for laborious manual …

The increasing complexity of tasks in robotics demands efficient strategies for multitask and
continual learning. Traditional models typically rely on a universal policy for all tasks, facing …

This letter presents a differential geometric control approach that leverages SE (3) group
invariance and equivariance to increase transferability in learning robot manipulation tasks …

Safe Reinforcement Learning (RL) aims to find a policy that achieves high rewards while
satisfying cost constraints. When learning from scratch, safe RL agents tend to be overly …

Training robot policies in the real world can be unsafe, costly, and difficult to scale.
Simulation serves as an inexpensive and potentially limitless source of training data, but …

We present FORGE, a method that enables sim-to-real transfer of contact-rich manipulation
policies in the presence of significant pose uncertainty. FORGE combines a force threshold …

This study focuses on the locomotion capability improvement in a tendon-driven soft
quadruped robot through an online adaptive learning approach. Leveraging the inverse …

Contact-rich manipulation tasks often exhibit a large sim-to-real gap. For instance, industrial
assembly tasks frequently involve tight insertions where the clearance is less than 0.1 mm …