Reinforcement learning (RL) has emerged as a powerful paradigm for fine-tuning Large
Language Models (LLMs) for conditional text generation. In particular, recent LLMs such as …

This letter presents a new learning framework that leverages the knowledge from imitation
learning, deep reinforcement learning, and control theories to achieve human-style …

In this paper, we present a novel method for achieving dexterous manipulation of complex
objects, while simultaneously securing the object without the use of passive support …

Exploration in environments with sparse feed-back remains a challenging research problem
in reinforcement learning (RL). When the RL agent explores the environment randomly, it …

AlphaZero is a self-play reinforcement learning algorithm that achieves superhuman play in
chess, shogi, and Go via policy iteration. To be an effective policy improvement operator …

We present a method for enabling Reinforcement Learning of motor control policies for
complex skills such as dexterous manipulation. We posit that a key difficulty for training such …

The reinforcement learning algorithms that focus on how to compute the gradient and
choose next actions, are effectively improved the performance of the agents. However, these …

Reinforcement learning (RL) fundamentally focuses on teaching agents how to make
decisions by interacting with an environment. Unlike supervised learning approaches that …

Fixtureless assembly may be necessary in some manufacturing tasks and environments due
to various constraints but poses challenges for automation due to nondeterministic …

Reinforcement learning (RL) has emerged as a powerful paradigm for fine-tuning Large
Language Models (LLMs) for text generation. In particular, recent LLMs such as ChatGPT …