DexMV: Imitation Learning for Dexterous Manipulation from Human Videos

By Yuzhe Qin, Yueh-Hua Wu, Shaowei Liu, Hanwen Jiang, Ruihan Yang, Yang Fu, Xiaolong Wang. European Conference on Computer Vision, 2021

328 citations recorded by the source corpus.

. While significant progress has been made on understanding hand-object interactions in computer vision, it is still very challenging for robots to perform complex dexterous manipulation. In this paper, we propose a new platform and pipeline DexMV ( Dex terous M anipulation from V ideos) for imitation learning. We design a platform with: (i) a simulation system for complex dexterous manipulation tasks with a multi-finger robot hand and (ii) a computer vision system to record large-scale demonstrations of a human hand conducting the same tasks. In our novel pipeline, we extract 3D hand and object poses from videos, and propose a novel demonstration translation method to convert human motion to robot demonstrations. We then apply and benchmark multiple imitation learning algorithms with the demonstrations. We show that the demonstrations can indeed improve robot learning by a large margin and solve the complex tasks which reinforcement learning alone cannot solve. More details can be found in the project page.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/93230bed6246400949ac514df46fc1c56af80ec2

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Citations in the corpus

Masked Visual Pre-training for Motor Control (2022, background): “…hands but rely on explicit state estimation (Handa et al., 2020; Huang et al., 2021), expert policies (Chen et al., 2021a), human demonstrations (Ra-jeswaran et al., 2018; Radosavovic et al., 2021; Qin et al., 2021), human priors (Mandikal & Grauman, 2021), or models (Nagabandi et al., 2019).”
Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots (2024, methodology): “To infer action data from passive human video, previous works resort to hand pose detectors [44, 1, 38, 28], or combining human videos with in-domain teleoperated robot data to predict actions [33, 20, 34, 28].”
GELLO: A General, Low-Cost, and Intuitive Teleoperation Framework for Robot Manipulators (2023, unknown): “• Portable: The diversity of demonstration data collected in different tasks and environments is critical to the final performance of the learning system.”
Affordances from Human Videos as a Versatile Representation for Robotics (2023, background): “Recently, works have shown alternate ways to provide human demonstrations, via hand pose estimation and retargeting [5, 95, 110, 112, 126] in robot hands, but are mostly restricted to tabletop setups.”