[Paper Review] RoboHive: A Unified Framework for Robot Learning

Vikas Kumar, Rutav Shah+6 more|arXiv (Cornell University)|Oct 10, 2023

Robot Manipulation and LearningEngineering3 citations

TL;DR

RoboHive is an open-source, unified framework for robot learning that integrates diverse simulated and real-world environments with high physics fidelity and visual diversity. It provides a standardized, gym-like API, hardware abstraction via a unified RobotClass, teleoperation support, expert demonstrations (RoboSet), and comprehensive baselines—enabling seamless simulation-to-reality transfer and accelerating research in imitation, reinforcement, and multi-task learning.

ABSTRACT

We present RoboHive, a comprehensive software platform and ecosystem for research in the field of Robot Learning and Embodied Artificial Intelligence. Our platform encompasses a diverse range of pre-existing and novel environments, including dexterous manipulation with the Shadow Hand, whole-arm manipulation tasks with Franka and Fetch robots, quadruped locomotion, among others. Included environments are organized within and cover multiple domains such as hand manipulation, locomotion, multi-task, multi-agent, muscles, etc. In comparison to prior works, RoboHive offers a streamlined and unified task interface taking dependency on only a minimal set of well-maintained packages, features tasks with high physics fidelity and rich visual diversity, and supports common hardware drivers for real-world deployment. The unified interface of RoboHive offers a convenient and accessible abstraction for algorithmic research in 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. Details: https://sites.google.com/view/robohive

Motivation & Objective

• To address the fragmented landscape of robot learning software by unifying diverse environments, algorithms, and hardware interfaces into a single, maintainable ecosystem.
• To lower the barrier to entry for researchers by providing a consistent, accessible API and out-of-the-box support for simulation and real-world deployment.
• To enable robust benchmarking through standardized metrics, expert demonstrations (RoboSet), and baseline results across multiple task families.
• To support visual generalization and real-world transfer by emphasizing high physics fidelity and rich visual diversity in environments.
• To foster reproducibility and community-driven development through versioned releases, CI testing, and extensive documentation.

Proposed method

• The framework introduces a unified RobotClass abstraction that abstracts both simulated and physical robots, enabling seamless simulation-to-reality transfer via a single configuration flag.
• It leverages MuJoCo for high-fidelity physics simulation and integrates rich visual assets, textures, and scene compositions to enhance visual diversity.
• Teleoperation is natively supported via keyboard, space mouse, and VR controllers, enabling collection of human-annotated expert demonstrations (RoboSet) for imitation learning.
• The platform provides a gym-like API for integration with reinforcement and imitation learning algorithms, ensuring compatibility with existing RL libraries like TorchRL and mjRL.
• Visual domain randomization and scene layout randomization are natively supported in key environments to improve robustness to visual variation.
• All environments are version-controlled with continuous integration, ensuring reliability and reproducibility across software releases.

Figure 1: A subset of RoboHive ’s task-suites. [in clockwise order] Multi-Task Suite : Environments facilitating multiple tasks at once. Arms Suite : Diverse arms (w/ grippers) exposed to tabletop manipulation tasks. Hand Suite : Diverse hands exposed to tasks requiring dexterity. Locomotion Suite :

Experimental results

Research questions

• RQ1 How can a unified software framework streamline the development and benchmarking of robot learning algorithms across diverse tasks and hardware?
• RQ2 To what extent does high visual and physical fidelity in simulated environments improve policy generalization and real-world transfer?
• RQ3 Can a standardized, accessible interface accelerate research in imitation, multi-task, and hierarchical reinforcement learning?
• RQ4 How effective are expert demonstrations collected via teleoperation in enabling sample-efficient learning across complex manipulation and locomotion tasks?
• RQ5 What is the impact of visual diversity and domain randomization on the robustness of vision-based control policies?

Key findings

• RoboHive supports a wide range of tasks across manipulation, locomotion, dexterous hand control, musculoskeletal agents, and deformable objects, with environments built on MuJoCo for high physics fidelity.
• The framework enables seamless simulation-to-reality transfer via a single flag change using the unified RobotClass abstraction, significantly reducing deployment overhead.
• RoboSet, a large-scale human-teleoperated dataset of 12 kitchen skills, is open-sourced and supports research in offline and imitation learning.
• Baselines for commonly used algorithms are provided and versioned, with performance tracking across releases to ensure reproducibility and detect regressions.
• Experiments show that visual representations underperform proprioceptive-only baselines in locomotion tasks (e.g., ROBEL with D’Kitty), highlighting the challenge of visual generalization.
• In dexterous manipulation (e.g., Pen, Relocate), behavior cloning with only 25 demonstrations struggles, underscoring the difficulty of learning multimodal action distributions from limited expert data.

Figure 2: RoboHive presents environments with rich visual diversity to aid research in visual generalization and places high emphasis on physical realism to facilitate the real-world transfer.