Extreme dynamic symmetry enables omnidirectional and multifunctional robots
Researchers propose dynamic symmetry, quantified by dynamic isotropy, as a measure of uniformity in a robot's attainable center-of-mass accelerations. Through simulations and physical experiments, high dynamic symmetry improves trajectory tracking, task success, robustness, resilience, and energy efficiency. The Argus family of spherical robots, especially a 20-legged variant with near-extreme dynamic isotropy, demonstrates orientation-invariant locomotion, agile terrain traversal, rapid self-stabilization, and resilience to actuator failures.
Article intelligence
Key points
- Dynamic symmetry is defined as uniformity of a robot's attainable center-of-mass accelerations, measured via dynamic isotropy.
- Over 1,000 simulated morphologies show high dynamic symmetry consistently improves performance, with benefits peaking near the theoretical limit.
- The Argus robot family uses radially oriented linear actuators; a 20-legged physical prototype achieves near-extreme dynamic isotropy.
- The prototype exhibits omnidirectional perception, object interaction, and robust performance on uncertain terrain and under partial failures.
Why it matters
This matters because dynamic symmetry is defined as uniformity of a robot's attainable center-of-mass accelerations, measured via dynamic isotropy.
Technical impact
May affect research directions, evaluation methods, open-source reproduction, and productization paths.
[2605.29254] Extreme dynamic symmetry enables omnidirectional and multifunctional robots
[Submitted on 28 May 2026]
Title:Extreme dynamic symmetry enables omnidirectional and multifunctional robots
View a PDF of the paper titled Extreme dynamic symmetry enables omnidirectional and multifunctional robots, by Jiaxun Liu and 2 other authors
View PDF HTML (experimental)
Abstract:Symmetry is a central organizing principle in natural systems, yet its use as a unifying design strategy in robotics has largely remained limited to geometric form. We show that symmetry can instead be leveraged at the level of dynamic actuation capability. We introduce dynamic symmetry, the uniformity of a robot's attainable center-of-mass accelerations, and formalize it through a measure coined as dynamic isotropy. Across more than 1000 simulated morphologies, we found that higher dynamic symmetry consistently improved trajectory tracking, task success, robustness, resiliency, and energy efficiency, with the benefits becoming most pronounced as dynamic isotropy approached its theoretical limit. To study this regime systematically, we developed Argus, a family of spherical robots designed to explore the effects of increasing dynamic symmetry. Members of the Argus family vary in their actuation geometry and dynamic symmetry level while sharing a common architectural principle: radially oriented linear actuators that directly shape the robot's center-of-mass dynamics. Among them, we built a physical 20-leg Argus variant that achieved near-extreme dynamic isotropy and demonstrated orientation-invariant locomotion, agile traversal of cluttered and deformable terrain, rapid self-stabilization, and resilience to partial actuator failures. Its distributed sensing further enabled omnidirectional perception and object interaction during continuous motion. These results show that designing robots for symmetry not only in morphology but also in their attainable dynamics provides a powerful and general pathway toward agility, robustness, and multifunctionality in uncertain terrestrial and extraterrestrial environments.
Comments: Published in Science Robotics (2026). Our project website is at:this https URL
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.29254 [cs.RO]
(or arXiv:2605.29254v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.29254
arXiv-issued DOI via DataCite (pending registration)
Journal reference: Science Robotics 11, eaec1725 (2026)
Submission history
From: Jiaxun Liu [view email] [v1] Thu, 28 May 2026 02:15:58 UTC (18,108 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled Extreme dynamic symmetry enables omnidirectional and multifunctional robots, by Jiaxun Liu and 2 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.RO
new | recent | 2026-05
Change to browse by:
cs cs.AI
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?)