OPENTECHNICAL PUBLICATION
OT-2026-000000628

Learning manipulation through jacobian matrix of stiffness and impedance control

Published: 2026-09-15T19:03:10.839734Z
Permanent URL: https://opentechnical.org/p/OT-2026-000000628
Categories: Robotics & Automation > Robot Architectures

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ABSTRACT

The present disclosure provides a method of learning physical interactions in
robotic manipulation, comprising formulating a learning representation that
includes a spatial force of a robotic manipulator, a spatial stiffness, and a
spatial damping of the manipulator, wherein the learning representation is
used by an artificial intelligence agent to interpret or learn the dynamic
interaction between the robotic manipulator and an object or environment. The
spatial stiffness and spatial damping are derived from joint-space stiffness
and damping parameters using Jacobian-based transformations. The learning
representation may be provided in forms comprising spatial force vectors,
stiffness matrices, damping matrices, end effector pose data, gripper force
measurements, and vision sensor data. The representation serves as input to
artificial intelligence learning algorithms including reinforcement learning,
supervised learning, or imitation learning, enabling the agent to learn
control policies for manipulating objects or regulating contact forces.

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OpenTechnical publication OT-2026-000000628
Published 2026-09-15T19:03:10.839734Z
https://opentechnical.org/p/OT-2026-000000628