Reinforcement learning is a promising approach to developing hard-to-engineer adaptive
solutions for complex and diverse robotic tasks. However, learning with real-world robots is …

Through many recent successes in simulation, model-free reinforcement learning has
emerged as a promising approach to solving continuous control robotic tasks. The research …

Reinforcement learning (RL) algorithms hold the promise of enabling autonomous skill
acquisition for robotic systems. However, in practice, real-world robotic RL typically requires …

The success of reinforcement learning for real world robotics has been, in many cases
limited to instrumented laboratory scenarios, often requiring arduous human effort and …

In robotics, the ultimate goal of reinforcement learning is to endow robots with the ability to
learn, improve, adapt and reproduce tasks with dynamically changing constraints based on …

In recent years, significant progress has been made in the field of robotic reinforcement
learning (RL), enabling methods that handle complex image observations, train in the real …

Recent successes aside, reinforcement learning (RL) still faces significant challenges in its
application to the real-world robotics domain. Guiding the learning process with additional …

Reinforcement learning (RL) provides a naturalistic framing for learning through trial and
error, which is appealing both because of its simplicity and effectiveness and because of its …

Deep Reinforcement Learning (DRL) has been applied successfully to many robotic
applications. However, the large number of trials needed for training is a key issue. Most of …

ROBEL is an open-source platform of cost-effective robots designed for reinforcement
learning in the real world. ROBEL introduces two robots, each aimed to accelerate …