Proprioceptive Invariant State Estimation for Humanoid Robots on Non-Inertial Ground
This paper presents an invariant extended Kalman filtering (InEKF) approach for real-time state estimation of humanoid robots on non-inertial ground using only onboard proprioceptive sensing. It estimates the robot's base position and velocity relative to the moving ground without direct ground motion measurements, using foot-mounted IMUs and kinematic constraints. Experiments on Digit robot show 96% faster convergence and 80% lower position errors on swaying ground, and average errors under 9 cm on rotating ground.
[2606.19512] Proprioceptive Invariant State Estimation for Humanoid Robots on Non-Inertial Ground
[Submitted on 17 Jun 2026]
Title:Proprioceptive Invariant State Estimation for Humanoid Robots on Non-Inertial Ground
View a PDF of the paper titled Proprioceptive Invariant State Estimation for Humanoid Robots on Non-Inertial Ground, by Falak Mandali and 2 other authors
View PDF HTML (experimental)
Abstract:This paper presents an invariant extended Kalman filtering (InEKF) approach for real-time state estimation of humanoid robots operating on non-inertial ground using only onboard proprioceptive sensing. The proposed approach estimates the robot's base position and velocity relative to the moving ground frame without requiring direct measurements of ground motion or externally mounted sensors. By exploiting kinematic constraints at the stance foot through foot-mounted IMUs, the filter accounts for ground-induced nonlinearities in the process and measurement models while remaining fully proprioceptive. The estimator is formulated to admit a right-invariant measurement model, enabling favorable error dynamics under large initial uncertainties. Observability analysis establishes conditions under which the robot's relative base position and velocity are observable with respect to the non-inertial ground frame. Experiments with the Digit humanoid robot standing and squatting atop a swaying and pitching ground showcase a 96% speedup in convergence rate and an 80% reduction in position estimate errors over existing InEKFs. Walking experiments on a uni-axially rotating ground achieve an average estimation error of less than 9 cm for an initial error of up to 1 m.
Subjects:
Robotics (cs.RO); Systems and Control (eess.SY)
Cite as: arXiv:2606.19512 [cs.RO]
(or arXiv:2606.19512v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.19512
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zijian He [view email] [v1] Wed, 17 Jun 2026 18:53:48 UTC (4,730 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled Proprioceptive Invariant State Estimation for Humanoid Robots on Non-Inertial Ground, by Falak Mandali and 2 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.RO
new | recent | 2026-06
Change to browse by:
cs cs.SY eess eess.SY
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)