# Introducing Samsara-VLA · Good AI Labs

> Robot control with memory, in two compact models.

URL: https://www.goodailabs.com/blog/samsara-capability-and-compute/
Date: 2026-10-05

Two models. Four kinds of manipulation. Selected successful episodes from both released checkpoints. Scene and wrist cameras are shown together.
Complete recorded actions, replayed in the original simulator. Playback follows simulation time and excludes inference waits. The full evaluation determines the success rates.

Spatial: Find the object described by its position.
Samsara-VLA · Spatial. Complete recorded evaluation episode from the released checkpoint. Selected successful example. Playback follows simulation time and excludes inference waits.
Recording: https://www.goodailabs.com/media/samsara/release/base/spatial.mp4
Samsara-VLA Tiny · Spatial. Complete recorded evaluation episode from the released checkpoint. Selected successful example. Playback follows simulation time and excludes inference waits.
Recording: https://www.goodailabs.com/media/samsara/release/tiny/spatial.mp4

Object: Select the named object among distractors.
Samsara-VLA · Object. Complete recorded evaluation episode from the released checkpoint. Selected successful example. Playback follows simulation time and excludes inference waits.
Recording: https://www.goodailabs.com/media/samsara/release/base/object.mp4
Samsara-VLA Tiny · Object. Complete recorded evaluation episode from the released checkpoint. Selected successful example. Playback follows simulation time and excludes inference waits.
Recording: https://www.goodailabs.com/media/samsara/release/tiny/object.mp4

Goal: Change the scene to match the instruction.
Samsara-VLA · Goal. Complete recorded evaluation episode from the released checkpoint. Selected successful example. Playback follows simulation time and excludes inference waits.
Recording: https://www.goodailabs.com/media/samsara/release/base/goal.mp4
Samsara-VLA Tiny · Goal. Complete recorded evaluation episode from the released checkpoint. Selected successful example. Playback follows simulation time and excludes inference waits.
Recording: https://www.goodailabs.com/media/samsara/release/tiny/goal.mp4

Long: Carry out an instruction with several actions.
Samsara-VLA · Long. Complete recorded evaluation episode from the released checkpoint. Selected successful example. Playback follows simulation time and excludes inference waits.
Recording: https://www.goodailabs.com/media/samsara/release/base/long.mp4
Samsara-VLA Tiny · Long. Complete recorded evaluation episode from the released checkpoint. Selected successful example. Playback follows simulation time and excludes inference waits.
Recording: https://www.goodailabs.com/media/samsara/release/tiny/long.mp4


Samsara-VLA is a robot policy that carries context from one observation to the
next. It uses two camera views and an instruction to pick, place, open and
complete sequences of actions. The videos above show complete successful
episodes from the two released models.

## Two sizes. One policy interface.

The 217M-parameter Samsara-VLA completed **1,890 of 2,000 native LIBERO episodes**.
Samsara-VLA Tiny uses 136M parameters and completed **1,786** under the same
evaluation protocol.


Samsara-VLA: 2.17428023e+08 parameters; 94.50% overall success (1890/2000).

- libero_10: 89.8% (449/500).

- libero_goal: 94.6% (473/500).

- libero_object: 99.4% (497/500).

- libero_spatial: 94.2% (471/500).


Samsara-VLA Tiny: 1.36519607e+08 parameters; 89.30% overall success (1786/2000).

- libero_10: 76.0% (380/500).

- libero_goal: 93.2% (466/500).

- libero_object: 98.2% (491/500).

- libero_spatial: 89.8% (449/500).



The difference is clearest on longer tasks: **89.8%** success for the full model
and **76.0%** for Tiny. Object selection remains strong in both, at **99.4%** and
**98.2%**. Tiny reduces the parameter count by **37.2%**.

## Context between actions

Each decision combines a fresh view of the scene with a compact memory of
previous observations. The policy predicts twelve control actions, executes
them, then observes again. Its memory resets when a new task begins.

## A policy you can run locally

Both models share a Python SDK for CPU and CUDA execution, with ONNX exports
for browser inference. The browser preview runs on your device using WebGPU
or WebAssembly CPU after loading the model.

These are experimental simulation policies. The native results cover forty
trained task templates with one evaluation seed; browser demonstrations are
separate. Models and the preview are available through research access. Read
the [full model card](https://huggingface.co/gai-labs/samsara-vla/blob/main/MODEL_CARD.md)
for methods and limitations, or explore the research page for suite results
and comparisons with published models.

Contact: research@goodailabs.com