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Tri Lbm

Tri Lbm is a Python implementation of Toyota Research Institute’s Large Behavioral Model architecture for dexterous robotic manipulation. Its example accepts text, images, pose and action data for loss calculation and backpropagation, then shows action sampling after training.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Tri Lbm — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when investigating a Python implementation of a large behavioral model for dexterous manipulation, especially when you can provide training inputs and work through the training process before evaluating sampled actions.

Who it is for

  • Robotics researchers exploring dexterous manipulation models.
  • Developers prototyping language- and image-conditioned action generation.

Documented uses

  • Initialize a behavioral model with configured action and pose dimensions.
  • Calculate a training loss from text, image, pose and action inputs and run backpropagation.
  • Sample a sequence of actions from text, images and pose after training.
Before you choose

Requirements & limits

The repository is public and lists an MIT license. Its README documents installation with pip.

  • The README marks the implementation as work in progress.
  • The example requires training before its action-sampling stage.
  • The shown inputs are tensors and text; the README does not provide a complete hardware deployment workflow.
  • A linked data-normalization note may be relevant when preparing training data.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install the Python package

Install the TRI-LBM package and import its model class, following the repository’s documented setup.

Before you start

  • A Python environment with pip.
  • PyTorch is imported in the README usage example.

Steps

  1. Run the command below in the environment.

    Documented command
    pip install TRI-LBM

  2. In Python, import torch and LBM from TRI_LBM.

Expected outcome

The documented package installation command has been run, and the example’s model class import is available to try.

Guide limitations

  • The README does not specify Python or PyTorch version requirements.
  • Installation and import are described in the repository; this guide does not report an execution test.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
15 October 2026
Original website title
lucidrains/TRI-LBM · Official homepage title · retained in its source language
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