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ocbench

A controllable benchmark for robotic manipulation

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This is 1 of 89 launches in robotics simulation and embodied AI — see how it stacks up on momentum and crowding →

1795 other launches read as similar to this one →

Details

External ID
1405088237
Source
GITHUB
Company
—
Product
ocbench
Website domain
seohong.me
Launched
Oct. 5, 2026
Cohort
—
Upvotes
45
Upvotes percentile
0.7095687331536388
Tags
—
Fetched at
Oct. 6, 2026, 5:02 p.m.
Updated at
Oct. 6, 2026, 5:02 p.m.

Enrichment

Niche
robotics simulation and embodied AI
Vertical
Horizontal
Function
Observability & eval
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
benchmark suite for robotic manipulation
Manually corrected
False

Could you build this?

No This is academic robotic manipulation research from UC Berkeley requiring deep expertise in reinforcement learning, MuJoCo physics simulation, and GPU kernel programming.

What it would actually take: Requires implementing 28 physical manipulation tasks and scripted trajectory policies using GPU-accelerated physics (MJWarp / MuJoCo). Developing realistic non-Markovian continuous robotic control benchmarks requires deep domain expertise in reinforcement learning, robotics dynamics, and high-performance simulation engineering.

Competitors

Other products that read as similar to this one — 1795 launches clear the similarity bar, closest 8 shown.

Attention rank: #470 of 1796 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 341 days after the earliest competitor.

Other launches for this product

Same idea, different domain

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