Why manipulation, not hardware, is robotics' real bottleneck
The case for a dedicated manipulation layer.
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Blog
Notes on giving robots hands
Deep, no-hype writing on manipulation, cross-embodiment learning, and the robot economy.
From the GripSim engineering team
NVIDIA InceptionIsaac LabGR00TJetson ThorOmniverseCosmos
Latest
Recent articles
One model, many hands: what cross-embodiment really means
How a single policy generalizes across morphologies.
Read more →The data flywheel that compounds with every grasp
Why deployment data is the moat.
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Topics
What we write about
The hard parts of robot manipulation.
Foundation models
VLA policies and morphology transfer.
Contact & touch
World models and closed-loop control.
Data & moats
Flywheels and proprietary datasets.
Featured
Start with the bottleneck
If you read one post, read why manipulation is the blocker for the robot economy.
- The real bottleneck
- Why now
- What changes next
Cadence
How we publish
1
Research
We benchmark and test.
2
Write
We share what we learn.
3
Discuss
You reply and push back.
4
Iterate
We update with new data.
Weekly
New posts
Open
No paywall
Honest
No hype
Readers
On the blog
“Some of the clearest robotics writing around.”
“I forward these to my team.”
“Benchmarks I can actually trust.”
Blog FAQ
Questions
How do I subscribe?
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Can I republish?
Reach out — we're open to it with attribution.
Do you take guest posts?
Occasionally, from practitioners.
Why GripSim
Built for production robotics
The manipulation layer teams ship with, not a research demo.
Cross-embodiment by design
One policy runs across many grippers and hands — no per-robot rewrite.
Closed-loop with touch
Tactyl predicts slip and force so grasps react before they fail.
Runs on the robot
Reflex executes on Jetson at control-loop latency, with safety limits.
A moat that compounds
Every deployment feeds the shared model through the data flywheel.
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