Large Language Models (LLMs) have been shown to be capable of performing high-level
planning for long-horizon robotics tasks, yet existing methods require access to a pre …

We present a novel Learning from Demonstration (LfD) method, Deformable Manipulation
from Demonstrations (DMfD), to solve deformable manipulation tasks using states or images …

Approaching robotic cloth manipulation using reinforcement learning based on visual
feedback is appealing as robot perception and control can be learned simultaneously …

Robotic manipulation is challenging due to discontinuous dynamics, as well as high-
dimensional state and action spaces. Data-driven approaches that succeed in manipulation …

Model-based RL is a promising approach for real-world robotics due to its improved sample
efficiency and generalization capabilities compared to model-free RL. However, effective …

Some Learning from Demonstrations (LfD) methods handle small mismatches in the action
spaces of the teacher and student. Here we address the case where the teacher's …

Recent works in robotic manipulation through reinforcement learning (RL) or imitation
learning (IL) have shown potential for tackling a range of tasks eg, opening a drawer or a …

A long-standing goal in AI is to build agents that can solve a variety of tasks across different
environments, including previously unseen ones. Two dominant approaches tackle this …

Multi-task learning has expanded the boundaries of robotic manipulation, enabling the
execution of increasingly complex tasks. However, policies learned through reinforcement …

Reinforcement Learning (RL) has shown remarkable progress in simulation environments,
yet its application to real-world robotic tasks remains limited due to challenges in exploration …