DexSuite: Comprehensive Framework for Advanced Robotic Dexterity

Paper: DexSuite: An All-in-One Framework for Dexterous Manipulation Across Arms, Hands, and Tasks

Authors: Anas Houssaini, Hugo He, Junming Shi, Amin Abyaneh, Ricardo Chahine, Shuo Wen, Mohamad H. Danesh, Charles-'Etienne Fr'echette, Mariana Sosa Guzman, Mathias Desrochers, Rayan Houssaini, Adam Landa, Theophile Soulie, Charlotte Morissette, Annie Tims, Martino Russi, Jonathan Lussier, Gregory Dudek, Doina Precup, Julen Urain, Hsiu-Chin Lin, David Meger

Executive Summary

DexSuite is a newly developed simulation framework aimed at advancing the field of dexterous manipulation in robotics. This framework addresses a significant challenge in robotics: achieving human-level dexterity. Currently, many existing simulation tools do not comprehensively cover the entire workflow required for effective dexterous manipulation, which includes robot setup, demonstration collection, and policy evaluation.

Built on the Genesis simulator, DexSuite offers a robust platform featuring 30 distinct tasks. It incorporates 10 different robotic arms and 12 types of end-effectors, ranging from simple parallel-jaw grippers to advanced dexterous hands. This variety allows for the creation of over 100 unique single-arm robotic configurations and thousands of bimanual combinations. By providing this extensive range of tools, DexSuite could facilitate the exploration of various manipulation strategies and applications.

A significant aspect of DexSuite is its standardized environment application programming interface (API), which enhances usability and integration with other systems. Additionally, the framework includes integrated teleoperation capabilities and simulated tactile sensing, which can improve the robot's interaction with objects and environments. These features may enable researchers and developers to create more sophisticated manipulation techniques, potentially leading to better performance in real-world scenarios.

The research also introduces a benchmark consisting of 16 task-embodiment pairs that vary in contact complexity and operational horizons. This benchmark is designed to evaluate the capabilities of different robotic manipulation policies effectively. Moreover, the DexSuite framework comes with a dataset containing 4,700 successful manipulation episodes. These episodes have been generated from various sources, including human teleoperation, scripted expert actions, and learned policies. This dataset could serve as a valuable resource for training and testing new algorithms in dexterous manipulation.

DexSuite is released as open-source software, promoting collaboration and innovation in the robotics community. By making this tool publicly available, the authors encourage other researchers and developers to build upon their work, potentially accelerating advancements in the field. Videos and additional datasets related to DexSuite can be accessed at the provided website.

The implications of this research are significant. By improving simulation capabilities for dexterous manipulation, organizations in sectors such as manufacturing, healthcare, and service robotics may find opportunities to enhance automation processes. Increased dexterity in robots could lead to more efficient operations, reduced costs, and improved service delivery. While the results and benchmarks presented in this study are derived from simulations, the framework's open-source nature invites further exploration and potential real-world applications.

Academic Abstract

Human-level dexterity remains a central open problem in robotics, yet few simulation frameworks cover the full workflow of dexterous manipulation, from setting up a robot to collecting dexterous demonstrations and evaluating policies. We present DexSuite, an all-in-one simulation framework for dexterous manipulation built on the Genesis simulator. DexSuite provides 30 tasks and assembles its 10 arms and 12 end-effectors, from parallel-jaw grippers to dexterous hands, into more than 100 single-arm robots and thousands of bimanual pairs. It also ships a standardized environment API, integrated teleoperation, and simulated tactile sensing. Moreover, we define a benchmark of 16 task-embodiment pairs spanning diverse contact complexities and horizons, and release a dataset of 4,700 successful episodes in simulation from human teleoperation, scripted experts, and learned policies. We release DexSuite as open source and hope others will build on it. Videos and datasets are available at https://dexsuiteorg.github.io