RoboHive - A unified framework for robot learning

sites.google.com/view/robohive
RoboHive, is a modular framework for research in the field of Robot Learning and Embodied AI. RoboHive ecosystem encompasses a range of pre-existing and novel environments, including dexterous manipulation with the Shadow Hand, whole arm manipulation tasks with Franka and Fetch robots, and various quadruped loco-motion tasks. In comparison to previous works, RoboHive offers a streamlined and unified task interface, utilizes the latest simulation bindings, features tasks with rich visual diversity, and supports common hardware drivers for real-world developments. The unified interface of RoboHive offers researchers a convenient and accessible platform to study a multitude of learning paradigms such as imitation, reinforcement, multi-task, and hierarchical learning. Furthermore, RoboHive includes expert demonstrations and baseline results for most environments, providing a standard for benchmarking and comparisons.
Features:
- Most extensive and diverse task collection
- Fully customizable visually rich task designed for behavior generalization.
- Rewards agnostic task success metrics
- Support for diverse algorithmic families + pre-trained baselines
- Sim & hardware agnostic robot class for easy transition between the two
- Teleoperation support. Human+Expert dataset