Reinforcement learning (RL) agents typically learn tabula rasa, without prior knowledge of
the world. However, if initialized with knowledge of high-level subgoals and transitions …

Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within
the field of NLP, aimed at addressing limitations in existing frameworks while aligning with …

Abstract We present Skill Transformer, an approach for solving long-horizon robotic tasks by
combining conditional sequence modeling and skill modularity. Conditioned on egocentric …

Deep generative models (DGMs) have demonstrated great success across various domains,
particularly in generating texts, images, and videos using models trained from offline data …

This study investigates the use of adapters in reinforcement learning for robotic skill
generalization across multiple robots and tasks. Traditional methods are typically reliant on …

The full potential of large pretrained models remains largely untapped in control domains
like robotics. This is mainly because of the scarcity of data and the computational challenges …

Embodied robots which can interact with their environment and neighbours are increasingly
being used as a test case to develop Artificial Intelligence. This creates a need for …

Successfully addressing a wide variety of tasks is a core ability of autonomous agents
requiring flexibly adapting the underlying decision-making strategies and as we argue in this …

Continual and interactive robot learning is a challenging problem as the robot is present with
human users who expect the robot to learn novel skills to solve novel tasks perpetually with …

The increasing demand to apply reinforcement learning (RL) in safety-critical domains
accentuates the essential need for safe, robust, and versatile RL algorithms. This thesis …