3 accepted papers
Shared autonomy is the future of teleoperation as it reduces the teleoperator’s burden, enhances capabilities, and improves embodiment by offering seamless control of the robot. However, it remains rarely used, particularly with humanoid robots, as it faces numerous challenges. In this work, we intr
Imitation learning (IL) aims to enable robots to perform tasks autonomously by observing a few human demonstrations. Recently, a variant of IL, called In-Context IL, utilized off-the-shelf large language models (LLMs) as instant policies that understand the context from a few given demonstrations to
Reinforcement Learning (RL) methods have been proven successful in solving manipulation tasks autonomously. However, RL is still not widely adopted on real robotic systems because working with real hardware entails additional challenges, especially when using rigid position-controlled manipulators.