Reinforcement learning (RL), particularly its combination with deep neural networks,
referred to as deep RL (DRL), has shown tremendous promise across a wide range of …

AI alignment aims to make AI systems behave in line with human intentions and values. As
AI systems grow more capable, the potential large-scale risks associated with misaligned AI …

A central question in robotics is how to design a control system for an agile mobile robot.
This paper studies this question systematically, focusing on a challenging setting …

Artificial neural networks, deep-learning methods and the backpropagation algorithm form
the foundation of modern machine learning and artificial intelligence. These methods are …

Examples:• A predictive maintenance agent for industrial equipment that analyzes sensor
data to predict failures before they happen, scheduling maintenance only when needed and …

An open research question in robotics is how to combine the benefits of model-free
reinforcement learning (RL)—known for its strong task performance and flexibility in …

Over the last decade, the use of autonomous drone systems for surveying, search and
rescue, or last-mile delivery has increased exponentially. With the rise of these applications …

Collecting large amounts of real-world interaction data to train general robotic policies is
often prohibitively expensive, thus motivating the use of simulation data. However, existing …

Artificial intelligence (AI) has been commoditized. It has evolved from a specialty resource to
a readily accessible tool for cancer researchers. AI-based tools can boost research …

We introduce Diffusion Policy Policy Optimization, DPPO, an algorithmic framework
including best practices for fine-tuning diffusion-based policies (eg Diffusion Policy) in …