OPENTECHNICAL PUBLICATION
OT-2026-000001396

Training and/or utilizing machine learning model(s) for use in natural language based robotic control

Published: 2026-09-15T19:05:34.938258Z
Permanent URL: https://opentechnical.org/p/OT-2026-000001396
Categories: Robotics & Automation > Robot Architectures

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ABSTRACT

Techniques are disclosed that enable training a goal-conditioned policy based
on multiple data sets, where each of the data sets describes a robot task in a
different way. For example, the multiple data sets can include: a goal image
data set, where the task is captured in the goal image; a natural language
instruction data set, where the task is described in the natural language
instruction; a task ID data set, where the task is described by the task ID,
etc. In various implementations, each of the multiple data sets has a
corresponding encoder, where the encoders are trained to generate a shared
latent space representation of the corresponding task description. Additional
or alternative techniques are disclosed that enable control of a robot using a
goal-conditioned policy network. For example, the robot can be controlled,
using the goal-conditioned policy network, based on free-form natural language
input describing robot task(s).

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OpenTechnical publication OT-2026-000001396
Published 2026-09-15T19:05:34.938258Z
https://opentechnical.org/p/OT-2026-000001396