vikashplus/robohive

What it solves

RoboHive provides a unified framework for robot learning by consolidating various simulation environments and tasks into a single, standardized interface. It eliminates the need to manage multiple disparate simulation setups for different robotic tasks, allowing researchers to test agents across a wide range of robotic challenges.

How it works

The project uses the MuJoCo physics engine to simulate robotic environments. These environments are exposed via the OpenAI-Gym API, making them compatible with any gym-compatible reinforcement learning (RL) training framework, such as Stable Baselines, RLlib, and TorchRL.

Who it’s for

It is designed for researchers and developers working on robot learning, reinforcement learning, and musculoskeletal control who need a standardized set of benchmarks to train and evaluate their agents.

Highlights

• Diverse Environment Suites: Includes specialized collections for hand manipulation (dexterous manipulation), arm manipulation, musculoskeletal control (Myo-Suite and MyoDM-Suite), and multi-task learning (including RelayKitchen benchmarks).
• Gym Compatibility: Seamlessly integrates with popular RL frameworks due to its OpenAI-Gym API implementation.
• Extensible: Supports visual encoders like R3M, RRL, and VC.
• Broad Task Coverage: Covers everything from simple reaching tasks to complex dexterous manipulation and real-world locomotion benchmarks.