32 datasets found

The dataset used in the paper is a MuJoCo environment, with 13-states and 4-control inputs, nonlinear dynamics with polynomial dependency in the control inputs.

The dataset used in the paper for safe reinforcement learning for legged locomotion.

The dataset used in the paper for safe reinforcement learning for legged locomotion.

The dataset used in the paper is a collection of robot locomotion tasks with various constraints.

A near real-world benchmark for offline RL, which contains datasets from various domains with controlled sizes, and extra test datasets for policy validation.

The HandManipulateBlock environment from OpenAI gym robotics suite

The FetchPickAndPlace and HandManipulateBlock environments from OpenAI gym robotics suite

The FetchPush, FetchPickAndPlace and HandManipulateBlock environments from OpenAI gym robotics suite

The Funnel board task is a domain where a ball falls through a grid of obstacles onto one of five platforms. Every other row of obstacles consists of funnel-shaped objects,...

The Room runner task is a domain where an agent moves through a randomly generated map of rooms, which are observed in 2D from above. The agent follows the policy of always...

The dataset used in the paper is a simulated environment for event camera-based reinforcement learning. The dataset includes a car-like robot equipped with an event camera, and...

The dataset used in the paper is a collection of robot morphologies, each with a unique set of actuators and joints.

The dataset used in the paper is a collection of environments with multiple confounders, including mass, length, damping, and a crippled leg. The dataset is used to evaluate the...

The dataset used in the paper is a simulated robot environment.

The dataset used in the paper is a simulated self-driving car environment, a mountain car environment, and a robotic environment.

The dataset used in this paper is a set of simulated robotic locomotion tasks, including Walker2d-v3, HalfCheetah-v3, Ant-v3, and Humanoid-v3.

The dataset used for training a robotic agent to play the theremin instrument using time-dependent goals.

The dataset used in the paper is a set of motor babbling data, which is used to initialize the dynamics model and to optimize the control policies.

The dataset used in the paper is RLBench, a standard benchmark for vision-based robotics which has been shown to serve as a proxy for real-robot experiments.

The Continual World benchmark consists of ten realistic robotic manipulation tasks.