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ManiLoop

An open framework for closed-loop evaluation of VLA, VLM, and other robot policies across benchmarks, designed for fair and reproducible comparisons. Current integrations: MuJoCo tasks, LIBERO, GPT, and LeRobot.

Details

External ID
1372403574
Source
GITHUB
Company
—
Product
ManiLoop
Website domain
github.io
Launched
Sept. 16, 2026
Cohort
—
Upvotes
12
Upvotes percentile
0.3866256725595696
Tags
—
Fetched at
Sept. 20, 2026, 5:44 p.m.
Updated at
Sept. 20, 2026, 5:44 p.m.

Enrichment

Theme
embodied AI and robotics platforms
Vertical
Horizontal
Function
Observability & eval
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
closed-loop evaluation framework for robot policies
Manually corrected
False

Could you build this?

Partial The web UI and episode recording viewer can be vibe-coded, but the framework requires deep integration with physics simulators (MuJoCo), robotic manipulation benchmarks (LIBERO), and low-level robot control loops.

What it would actually take: A complete system requires a Python runtime coupling MuJoCo physics simulation with vision-language-action (VLA) model inference (e.g., ACT, Diffusion Policy) and TCP trajectory control. The hard parts are real-time closed-loop simulation step management, exact sensor proprioception pipelines, and reproducible benchmark evaluations without data leakage. Building it requires expertise in robotic simulation, kinematics/dynamics, and robotic learning frameworks (like LeRobot).

Competitors

Other products that read as similar to this one — 1409 launches clear the similarity bar, closest 8 shown.

Attention rank: #762 of 1410 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 322 days after the earliest competitor.

Other launches for this product

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