Physical AI
Kinesthetic Teaching
Teach Robots by Simply Guiding Them
Kinesthetic Teaching allows users to physically guide a robot through tasks while capturing synchronized motion, force, tactile, and visual data. Instead of writing code, operators demonstrate the desired behavior directly.
This bridges human expertise and robotic automation, accelerating deployment while producing structured datasets for Learning-from-Demonstration and Physical AI systems.
Why It Accelerates Development
Transforming demonstrations into structured training data.
No coding required
Program tasks through physical demonstration.
Rapid task setup
Reduce episode demonstration time from hours to minutes.
Natural human-robot interaction
Lower the technical barrier for operators.
Full multimodal capture
Record motion, force, tactile, and vision simultaneously.
AI-ready datasets
Synchronized data streams ready for training pipelines.
Synchronized Multimodal Intelligence
Captured Data
Each demonstration generates a structured, time-aligned dataset that captures both motion and physical interaction. All signals are recorded simultaneously, ensuring consistency and direct usability within AI training pipelines.
- Joint position, velocity & torque
- Cartesian pose & twist
- Wrench at TCP
- Fingertip tactile sensing
- Vision streams
- Text or audio annotations
Where Demonstrations Become Deployment
Typical Applications
Kinesthetic Teaching accelerates the development of learning-based manipulation by transforming expert guidance into scalable robotic capabilities.
- Learning from Demonstration
- Assembly tasks
- Surface finishing
- Research dataset generation
CLICK. COLLECT. INFER.
Turn expert demonstrations into structured data and scalable robotic intelligence.