This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Product upvotes vs the next 3
Waiting for data. Loading
Product comments vs the next 3
Waiting for data. Loading
Product upvote speed vs the next 3
Waiting for data. Loading
Product upvotes and comments
Waiting for data. Loading
Product vs the next 3
Loading
Kineforge AI/ML model for robotics
Train robot brains without demos on one GPU
Kineforge trains embodied control policies without human teleoperation demos. Frozen semantic priors + differentiable physics (MuJoCo MJX) + a 157K-parameter policy = training in hours on a single GPU, and CPU inference under 10ms. Structure over scale.
Hey Product Hunt 👋 I'm Illia, solo founder of Brightforge.
After Pixlie (our AI video studio), this is our research track: Kineforge.
The problem: teaching robots to act usually means one of two expensive paths — record thousands of human teleoperation demos, or run billion-parameter "robot brain" models that only live in the cloud. Both are slow, costly, and hard to deploy where it actually matters (warehouses, labs, hazardous sites).
Kineforge takes a different bet: structure over scale.
• No human demos. We train in simulation (MuJoCo MJX), not from teleop recordings. • Tiny trainable policy — 157K parameters (not billions), sitting on top of a frozen "knowledge layer" that gives the agent structure from day one. • Trains in hours on a single GPU, not weeks on a cluster. • Runs on a CPU in under 10ms — edge-deployable, no datacenter required.
On NVIDIA H200 we log stable task success ~0.70 with open metrics and honest simulator artifacts (345K+ logged steps, 0 crashes on a 1.5M-step run).
This is early and sim-first, real-robot pilots are next. I'd love feedback from robotics, RL, and embodied-AI folks, and intros to warehouse / lab automation teams who feel the data-cost pain. Imagine teaching a robot to do a job.
Today there are two slow, expensive ways: 1) Sit a human down for months to "puppet" the robot thousands of times (teleoperation). Slow. Costly. Boring. Doesn't scale. 2) Rent a giant AI "brain" with billions of settings that only runs in a massive cloud datacenter. Expensive every single time it thinks.
Kineforge does it differently. We give the robot a small, smart "starter brain" that already understands structure — like a kid who starts school already knowing the alphabet, instead of inventing language from zero.
Then it practices inside a fast video-game-like simulator (no real-world risk, no humans puppeting it). It learns the job in hours, on one computer chip — not weeks on a server farm.
The payoff for a business: • 10–50× less training data needed • 5–10× cheaper to develop each new skill • The finished "brain" is tiny — it runs on a cheap chip inside the machine, in the blink of an eye, with no cloud bill per move. Imagine teaching a robot to do a job.
Today there are two slow, expensive ways: 1) Sit a human down for months to "puppet" the robot thousands of times (teleoperation). Slow. Costly. Boring. Doesn't scale. 2) Rent a giant AI "brain" with billions of settings that only runs in a massive cloud datacenter. Expensive every single time it thinks.
Kineforge does it differently. We give the robot a small, smart "starter brain" that already understands structure — like a kid who starts school already knowing the alphabet, instead of inventing language from zero.
Then it practices inside a fast video-game-like simulator (no real-world risk, no humans puppeting it). It learns the job in hours, on one computer chip — not weeks on a server farm.
The payoff for a business: • 10–50× less training data needed • 5–10× cheaper to develop each new skill • The finished "brain" is tiny — it runs on a cheap chip inside the machine, in the blink of an eye, with no cloud bill per move.
So instead of "AI that's smart but ruinously expensive to run," you get "AI that's good enough AND cheap enough to actually put in thousands of machines." That's the difference between a demo and a business.
We've proven the economics in simulation. The next step is putting it into a real warehouse robot.
Under the hood: • POMDP control, actions factored into locomotion (16) / respiration (2) / head (2) / macro (4) = 24 discrete decisions per step. • Multi-stream policy: Fast (reflex), Valence (affect-modulated gains), Slow (topology-routed planning). • Frozen semantic prior (~2.6M params, 110 canonical symbols, 768-dim) supplies structured inductive bias; only ~157K policy params are trained (REINFORCE/PPO, JEPA-style + structural regularization). • Backend: MuJoCo MJX differentiable physics, JAX-native (no PyTorch in core), SkyPilot on Kubernetes, NVIDIA H200, MLflow tracking. • Results: ~0.70 task reward at 100K steps; ~159 (baseline) vs ~108 (grounded sensory) env-steps/s; CPU inference target <10ms. • Sim-only today; sim-to-real via Unity/ROS2 in progress. Priors withheld; sanitized eval protocol published.
So instead of "AI that's smart but ruinously expensive to run," you get "AI that's good enough AND cheap enough to actually put in thousands of machines." That's the difference between a demo and a business.
We've proven the economics in simulation. The next step is putting it into a real warehouse robot.
Under the hood: • POMDP control, actions factored into locomotion (16) / respiration (2) / head (2) / macro (4) = 24 discrete decisions per step. • Multi-stream policy: Fast (reflex), Valence (affect-modulated gains), Slow (topology-routed planning). • Frozen semantic prior (~2.6M params, 110 canonical symbols, 768-dim) supplies structured inductive bias; only ~157K policy params are trained (REINFORCE/PPO, JEPA-style + structural regularization). • Backend: MuJoCo MJX differentiable physics, JAX-native (no PyTorch in core), SkyPilot on Kubernetes, NVIDIA H200, MLflow tracking. • Results: ~0.70 task reward at 100K steps; ~159 (baseline) vs ~108 (grounded sensory) env-steps/s; CPU inference target <10ms. • Sim-only today; sim-to-real via Unity/ROS2 in progress. Priors withheld; sanitized eval protocol published. Model card and tech report is still wait endorsement on arXiv, because my CS teacher have only 2 endorsement by himself and to aprove you must have at least 3,sooo... (no fluff, real tables): https://illiaovcharenko1.github....
About Kineforge AI/ML model for robotics on Product Hunt
“Train robot brains without demos on one GPU”
Kineforge AI/ML model for robotics was submitted on Product Hunt and earned 9 upvotes and 1 comments, placing #19 on the daily leaderboard. Kineforge trains embodied control policies without human teleoperation demos. Frozen semantic priors + differentiable physics (MuJoCo MJX) + a 157K-parameter policy = training in hours on a single GPU, and CPU inference under 10ms. Structure over scale.
On the analytics side, Kineforge AI/ML model for robotics competes within Robots, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1M followers on Product Hunt. The dashboard above tracks how Kineforge AI/ML model for robotics performed against the three products that launched closest to it on the same day.
Who hunted Kineforge AI/ML model for robotics?
Kineforge AI/ML model for robotics was hunted by Illia Ovcharenko. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Kineforge AI/ML model for robotics including community comment highlights and product details, visit the product overview.
Illia Ovcharenko
Hey Product Hunt 👋 I'm Illia, solo founder of Brightforge.
After Pixlie (our AI video studio), this is our research track: Kineforge.
The problem: teaching robots to act usually means one of two expensive paths —
record thousands of human teleoperation demos, or run billion-parameter
"robot brain" models that only live in the cloud. Both are slow, costly, and
hard to deploy where it actually matters (warehouses, labs, hazardous sites).
Kineforge takes a different bet: structure over scale.
• No human demos. We train in simulation (MuJoCo MJX), not from teleop recordings.
• Tiny trainable policy — 157K parameters (not billions), sitting on top of a
frozen "knowledge layer" that gives the agent structure from day one.
• Trains in hours on a single GPU, not weeks on a cluster.
• Runs on a CPU in under 10ms — edge-deployable, no datacenter required.
On NVIDIA H200 we log stable task success ~0.70 with open metrics and honest
simulator artifacts (345K+ logged steps, 0 crashes on a 1.5M-step run).
This is early and sim-first, real-robot pilots are next. I'd love feedback from
robotics, RL, and embodied-AI folks, and intros to warehouse / lab automation
teams who feel the data-cost pain.
Imagine teaching a robot to do a job.
Today there are two slow, expensive ways:
1) Sit a human down for months to "puppet" the robot thousands of times
(teleoperation). Slow. Costly. Boring. Doesn't scale.
2) Rent a giant AI "brain" with billions of settings that only runs in a
massive cloud datacenter. Expensive every single time it thinks.
Kineforge does it differently. We give the robot a small, smart "starter brain"
that already understands structure — like a kid who starts school already
knowing the alphabet, instead of inventing language from zero.
Then it practices inside a fast video-game-like simulator (no real-world risk,
no humans puppeting it). It learns the job in hours, on one computer chip —
not weeks on a server farm.
The payoff for a business:
• 10–50× less training data needed
• 5–10× cheaper to develop each new skill
• The finished "brain" is tiny — it runs on a cheap chip inside the machine,
in the blink of an eye, with no cloud bill per move.
Imagine teaching a robot to do a job.
Today there are two slow, expensive ways:
1) Sit a human down for months to "puppet" the robot thousands of times
(teleoperation). Slow. Costly. Boring. Doesn't scale.
2) Rent a giant AI "brain" with billions of settings that only runs in a
massive cloud datacenter. Expensive every single time it thinks.
Kineforge does it differently. We give the robot a small, smart "starter brain"
that already understands structure — like a kid who starts school already
knowing the alphabet, instead of inventing language from zero.
Then it practices inside a fast video-game-like simulator (no real-world risk,
no humans puppeting it). It learns the job in hours, on one computer chip —
not weeks on a server farm.
The payoff for a business:
• 10–50× less training data needed
• 5–10× cheaper to develop each new skill
• The finished "brain" is tiny — it runs on a cheap chip inside the machine,
in the blink of an eye, with no cloud bill per move.
So instead of "AI that's smart but ruinously expensive to run," you get
"AI that's good enough AND cheap enough to actually put in thousands of
machines." That's the difference between a demo and a business.
We've proven the economics in simulation. The next step is putting it into a
real warehouse robot.
Under the hood:
• POMDP control, actions factored into locomotion (16) / respiration (2) /
head (2) / macro (4) = 24 discrete decisions per step.
• Multi-stream policy: Fast (reflex), Valence (affect-modulated gains),
Slow (topology-routed planning).
• Frozen semantic prior (~2.6M params, 110 canonical symbols, 768-dim) supplies
structured inductive bias; only ~157K policy params are trained (REINFORCE/PPO,
JEPA-style + structural regularization).
• Backend: MuJoCo MJX differentiable physics, JAX-native (no PyTorch in core),
SkyPilot on Kubernetes, NVIDIA H200, MLflow tracking.
• Results: ~0.70 task reward at 100K steps; ~159 (baseline) vs ~108
(grounded sensory) env-steps/s; CPU inference target <10ms.
• Sim-only today; sim-to-real via Unity/ROS2 in progress. Priors withheld;
sanitized eval protocol published.
So instead of "AI that's smart but ruinously expensive to run," you get
"AI that's good enough AND cheap enough to actually put in thousands of
machines." That's the difference between a demo and a business.
We've proven the economics in simulation. The next step is putting it into a
real warehouse robot.
Under the hood:
• POMDP control, actions factored into locomotion (16) / respiration (2) /
head (2) / macro (4) = 24 discrete decisions per step.
• Multi-stream policy: Fast (reflex), Valence (affect-modulated gains),
Slow (topology-routed planning).
• Frozen semantic prior (~2.6M params, 110 canonical symbols, 768-dim) supplies
structured inductive bias; only ~157K policy params are trained (REINFORCE/PPO,
JEPA-style + structural regularization).
• Backend: MuJoCo MJX differentiable physics, JAX-native (no PyTorch in core),
SkyPilot on Kubernetes, NVIDIA H200, MLflow tracking.
• Results: ~0.70 task reward at 100K steps; ~159 (baseline) vs ~108
(grounded sensory) env-steps/s; CPU inference target <10ms.
• Sim-only today; sim-to-real via Unity/ROS2 in progress. Priors withheld;
sanitized eval protocol published.
Model card and tech report is still wait endorsement on arXiv, because my CS teacher have only 2 endorsement by himself and to aprove you must have at least 3,sooo... (no fluff, real tables):
https://illiaovcharenko1.github....
Links
PDF (v1.0): Tech report PDF
Landing: GitHub Pages
GitHub: kineforge-portfolio
HuggingFace (HF Space): kineforge-demo
arXiv: pending endorsement (cs.RO) → then https://arxiv.org/abs/…
Contact: [email protected] · brightforge.ltd
Try the demo + ask me anything below 🙏