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 …

Reinforcement learning (RL), imitation learning (IL), and task and motion planning (TAMP)
have demonstrated impressive performance across various robotic manipulation tasks …

Dexterous object manipulation remains an open problem in robotics, despite the rapid
progress in machine learning during the past decade. We argue that a hindrance is the high …

Artificial touch would seem well-suited for Reinforcement Learning (RL), since both
paradigms rely on interaction with an environment. Here we propose a new environment …

Benchmarks offer a scientific way to compare algorithms using objective performance
metrics. Good benchmarks have two features:(a) they should be widely useful for many …

Autonomous fabric manipulation is a longstanding challenge in robotics, but evaluating
progress is difficult due to the cost and diversity of robot hardware. Using Reach, a cloud …

Current robots are either expensive or make significant compromises on sensory richness,
computational power, and communication capabilities. We propose to leverage …

Robot learning is widely accepted by academia and industry with its potentials to transform
autonomous robot control through machine learning. Inspired by widely used soft fingers on …

Reinforcement learning has shown great promise in robotics thanks to its ability to develop
efficient robotic control procedures through self-training. In particular, reinforcement learning …

Offline reinforcement learning (RL) is a promising direction that allows RL agents to pre-train
on large datasets, avoiding the recurrence of expensive data collection. To advance the …