dorsal/arxiv
View SchemaFiducial Exoskeletons: Image-Centric Robot State Estimation
| Authors | Cameron Smith, Basile Van Hoorick, Vitor Guizilini, Yue Wang |
|---|---|
| Categories | |
| ArXiv ID | 2601.08034vv1 |
| URL | https://arxiv.org/abs/2601.08034 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
Abstract
We introduce Fiducial Exoskeletons, an image-based reformulation of 3D robot state estimation that replaces cumbersome procedures and motor-centric pipelines with single-image inference. Traditional approaches - especially robot-camera extrinsic estimation - often rely on high-precision actuators and require time-consuming routines such as hand-eye calibration. In contrast, modern learning-based robot control is increasingly trained and deployed from RGB observations on lower-cost hardware. Our key insight is twofold. First, we cast robot state estimation as 6D pose estimation of each link from a single RGB image: the robot-camera base transform is obtained directly as the estimated base-link pose, and the joint state is recovered via a lightweight global optimization that enforces kinematic consistency with the observed link poses (optionally warm-started with encoder readings). Second, we make per-link 6D pose estimation robust and simple - even without learning - by introducing the fiducial exoskeleton: a lightweight 3D-printed mount with a fiducial marker on each link and known marker-link geometry. This design yields robust camera-robot extrinsics, per-link SE(3) poses, and joint-angle state from a single image, enabling robust state estimation even on unplugged robots. Demonstrated on a low-cost robot arm, fiducial exoskeletons substantially simplify setup while improving calibration, state accuracy, and downstream 3D control performance. We release code and printable hardware designs to enable further algorithm-hardware co-design.
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"date_created": "2026-02-17T05:53:16.276000Z",
"date_modified": "2026-02-17T05:53:16.276000Z",
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"abstract": "We introduce Fiducial Exoskeletons, an image-based reformulation of 3D robot state estimation that replaces cumbersome procedures and motor-centric pipelines with single-image inference. Traditional approaches - especially robot-camera extrinsic estimation - often rely on high-precision actuators and require time-consuming routines such as hand-eye calibration. In contrast, modern learning-based robot control is increasingly trained and deployed from RGB observations on lower-cost hardware.\n Our key insight is twofold. First, we cast robot state estimation as 6D pose estimation of each link from a single RGB image: the robot-camera base transform is obtained directly as the estimated base-link pose, and the joint state is recovered via a lightweight global optimization that enforces kinematic consistency with the observed link poses (optionally warm-started with encoder readings). Second, we make per-link 6D pose estimation robust and simple - even without learning - by introducing the fiducial exoskeleton: a lightweight 3D-printed mount with a fiducial marker on each link and known marker-link geometry.\n This design yields robust camera-robot extrinsics, per-link SE(3) poses, and joint-angle state from a single image, enabling robust state estimation even on unplugged robots. Demonstrated on a low-cost robot arm, fiducial exoskeletons substantially simplify setup while improving calibration, state accuracy, and downstream 3D control performance. We release code and printable hardware designs to enable further algorithm-hardware co-design.",
"arxiv_id": "2601.08034",
"authors": [
"Cameron Smith",
"Basile Van Hoorick",
"Vitor Guizilini",
"Yue Wang"
],
"categories": [
"cs.RO",
"cs.CV"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "Fiducial Exoskeletons: Image-Centric Robot State Estimation",
"url": "https://arxiv.org/abs/2601.08034",
"version": "v1"
},
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"variant": "snapshot-2026-01-17",
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