[Submitted on 19 Oct 2026]
Title:Embodied Playground: A Long-Horizon Agentic Robotics Benchmark for Physical, Strategic, and Spatial Reasoning
View a PDF of the paper titled Embodied Playground: A Long-Horizon Agentic Robotics Benchmark for Physical, Strategic, and Spatial Reasoning, by Feng Chen and 9 other authors
View PDF HTML (experimental)
Abstract:Foundation models offer a promising basis for robotic agents, but long-horizon tasks require them to sustain coherent reasoning and reliable physical execution across interdependent stages. We introduce Embodied Playground, a benchmark for evaluating how foundation-model agents coordinate physical, strategic, and spatial reasoning throughout such tasks. Agents must track task progress, satisfy intermediate constraints, and revise their plans using execution feedback as earlier actions shape subsequent decisions. The playground comprises 410 task environments spanning basic manipulation, sequential puzzles, and multi-stage household activities, with variations in planning depth, geometric constraints, and physical execution demands. We curate EP-100, a representative selection of 100 tasks across five suites and 16 task types, together with the EP-Easy and EP-Hard difficulty splits and the compact EP-20 selection. A shared protocol specifies observation and tool access, resource budgets, repeated trials, and independent checks of goal completion, task-rule compliance, and physical validity. Embodied Playground provides a unified framework for studying whether foundation-model agents can sustain effective reasoning and action over extended task horizons. this https URL
Comments: 38 pages, 2 figures
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2610.27039 [cs.RO]
(or arXiv:2610.27039v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.27039
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Feng Chen [view email] [v1] Mon, 19 Oct 2026 17:30:35 UTC (3,059 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled Embodied Playground: A Long-Horizon Agentic Robotics Benchmark for Physical, Strategic, and Spatial Reasoning, by Feng Chen and 9 other authors
View PDF
HTML (experimental)
TeX Source
view license
Additional Features
Audio Summary
Current browse context:
cs.RO
new | recent | 2026-10
Change to browse by:
cs
References & Citations
SciX
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?)