Zero-code AI robotics
Humans teach
the work.
Robots learn it.
Stop programming robots. Start showing them what to do. A worker demonstrates the task with our grippers - Ludis turns it into training data, and the robot learns to handle the real-world variation. Built on the Universal Manipulation Interface.
Before you commit
Is Ludis right for you?
Honest adoption starts with hard questions - about your task, your reliability bar, the economics, and us. Here's the checklist we'd work through with you. A fit is proven in a pilot, not a pitch.
07 Is my task a good match for this paradigm?
- Is it genuine manipulation - grasping, placing, inserting, folding - rather than something a simpler machine could do?
- How repetitive vs. variable is it? Learned policies tolerate some variation but get shaky with high variability or many edge cases.
- What precision and tolerance does it need? Sub-millimeter assembly is a different beast than "drop item in bin."
- Are the objects awkward - deformable, transparent, reflective, fragile, heavy? Those are classically hard.
- Is the work environment controlled, or messy and unpredictable?
- How long is the task horizon? A single short action is far easier than a long multi-step sequence.
- Can the policy hit your required cycle time, or only do it slowly?
03 What reliability do I actually need - and can it clear that bar?
- What success rate is acceptable - 95%? 99.9%? Many learned systems plateau below industrial-grade reliability for critical steps.
- What is the cost of a failure - scrap, safety, downstream defects?
- How are exceptions and recovery handled? Is there a human fallback when it gets confused?
03 Is the data / demonstration side feasible for me?
- Who collects the demonstrations, how many are needed, and how good do they have to be?
- When my product or task changes, do I have to re-demonstrate everything - and how painful is that?
- Do I need in-house ML/robotics talent to keep it healthy, or is it turnkey?
03 Does the economics work?
- Total cost: hardware, integration, licensing, ongoing maintenance - not just the sticker price.
- What labor or throughput am I actually displacing, and what is the payback period?
- How does it compare to my real alternatives: fixed automation, a conventionally programmed arm, or keeping humans?
03 How does it fit my operation?
- Footprint, power, safety guarding, and how it slots into my existing line / workflow.
- Uptime, support, SLAs - and who is on the hook when it breaks.
- Update path when tasks evolve.
03 Is Ludis itself a safe bet?
- How mature are they - funding, track record, real deployed customers in my industry?
- Since UMI is published research, what is their actual proprietary value-add and moat?
- Lock-in risk: what happens to my line if they fold or get acquired?
02 Safety, compliance, and people
- Human-robot safety, plus any regulatory constraints (food, pharma, medical, etc.).
- Workforce impact, change management, and acceptance.
01 Can I de-risk before committing?
- Is there a narrow, low-stakes pilot task I can run first - to get real data before betting the operation on it?
No fabricated benchmarks here - bring these questions and we'll answer them against your task, with a low-stakes pilot to get real numbers first.
The problem
Traditional automation can't keep up with real work.
Conventional robots are hard-coded for one rigid motion. Reality isn't rigid - parts move, deform, and arrive out of place. So the work stays manual.
Hard-coded robots
- ×Hundreds of engineering hours to program one task
- ×Breaks when a part shifts by a single millimeter
- ×One cell, one task - no tolerance for variation
- ×Every product change means re-programming
Show, don't program
- →A worker demonstrates the task in minutes
- →Learns to handle misaligned, moving, messy parts
- →The same skill transfers across the fleet
- →New tasks mean more demos - not more code
Teach once.
Robotize the rest.
Bring Ludis to your floor and start building a demonstration dataset this quarter.