LaWAM original

Participating organizations

Paper available

LaWAM is a latent world-action model for robot manipulation. A retained latent-action forward decoder predicts future DINO visual features, which condition action-chunk generation instead of reconstructed future video.

Research on dynamics-aware manipulation with Qwen3-VL-2B, frozen DINOv3 features, a latent world model and an action expert. Original packages include a LAM component, pretraining policy and separate LIBERO/RoboTwin post-trained policies. June 14 is the first research disclosure; October 25 is paper v2, not a verified new model or weight release date.

A newer version is available: LaWAM LeRobot ↗

Research material status

Paper available

Last checked: 2026-11-07

Papers & reports (1)

LaWAM: Latent World Action Models for Efficient Dynamics-Aware Robot Policies

Paper available

arXiv: 2606.15768DOI: 10.48550/arxiv.2606.15768
Authors & affiliations (12)

Author order: Original order

  1. Jialei Chen

    Not provided

  2. Kai Wang

    Not provided

  3. Kang Chen

    Not provided

  4. Shuaihang Chen

    Not provided

  5. Feng Gao

    Not provided

  6. Wenhao Tang

    Not provided

  7. Zhiyuan Li

    Not provided

  8. Weilin Liu

    Not provided

  9. Zhuyu Yao

    Not provided

First published
2026-06-14
Latest paper revision
v2 · 2026-10-25
Venue
CoRL 2026

Covered model versions

  • lawam · LaWAM original

    Original LaWAM/LaWM framework, benchmark-specific post-training and reported native research evaluations. Not an independent reproduction or a LeRobot converted-artifact result.

  • lawam · LaWAM LeRobot

    Original LaWAM method reference for the LeRobot implementation and converted packages. Converted LIBERO1970/2000=98.5% belongs to its exact artifact/card, not paper98.6% or original187ms latency.

Paper ↗PDF ↗

Access and availability

Code
Public access
Model files
Public access

Training / fine-tuning code

Access
Public access

Verified on · 2026-11-07

Model files

Model files
LaWAM LAM componentAccessWeights license
LaWAM original pretraining policyAccessWeights license
LaWAM original LIBERO SFTAccessWeights license
LaWAM original RoboTwin SFTAccessWeights license
Release information

SourceVerified on · 2026-11-07

At a glance

Model type
World-action model
Backbone
Qwen3-VL-2B + DINOv3 + LaWM
Input
Task instructions / RGB observations
Released
June 14, 2026
LaWAM paper v2 exact protocols ↗

LaWAM authors · Research paper

Publication date unknown · Accessed: 2026-11-07

Source records: 7

LaWAM paper v2 exact protocols

LaWAM authors · Research paper

Publication date unknown · Accessed: 2026-11-07

LaWAM paper v2 exact protocols

LaWAM authors · Research paper

Publication date unknown · Accessed: 2026-11-07

LaWAM paper v2 exact protocols

LaWAM authors · Research paper

Publication date unknown · Accessed: 2026-11-07

LaWAM paper v2 exact protocols

LaWAM authors · Research paper

Publication date unknown · Accessed: 2026-11-07

LaWAM paper v2 exact protocols

LaWAM authors · Research paper

Publication date unknown · Accessed: 2026-11-07

LaWAM paper v2 exact protocols

LaWAM authors · Research paper

Publication date unknown · Accessed: 2026-11-07

Source category does not verify the claims it contains.

Evaluation results

No verified independent benchmark results are recorded for this version or the selected weights.

Publisher evaluation · Reported; not independently reproduced

LaWAM original paper v2 · LIBERO Long ↗

Success rate: 97 %

Original LaWAM paper v2 developer-reported research results, version-scoped rather than assigned to a downloaded artifact or LeRobot conversion. Protocols and latency overhead exclusions remain explicit. Table1/AppendixF.2:40LIBERO tasks,50trials/task,2000trials total after benchmark-specific25k-step post-training. Mean of four equally sized10-task suites. Distinct from LeRobot native-format1970/2000=98.5% and MuJoCo3.3.2 evaluation.

Publisher evaluation · Reported; not independently reproduced

LaWAM original paper v2 · LIBERO Goal ↗

Success rate: 98.4 %

Original LaWAM paper v2 developer-reported research results, version-scoped rather than assigned to a downloaded artifact or LeRobot conversion. Protocols and latency overhead exclusions remain explicit. Table1/AppendixF.2:40LIBERO tasks,50trials/task,2000trials total after benchmark-specific25k-step post-training. Mean of four equally sized10-task suites. Distinct from LeRobot native-format1970/2000=98.5% and MuJoCo3.3.2 evaluation.

Publisher evaluation · Reported; not independently reproduced

LaWAM original paper v2 · LIBERO Object ↗

Success rate: 99.6 %

Original LaWAM paper v2 developer-reported research results, version-scoped rather than assigned to a downloaded artifact or LeRobot conversion. Protocols and latency overhead exclusions remain explicit. Table1/AppendixF.2:40LIBERO tasks,50trials/task,2000trials total after benchmark-specific25k-step post-training. Mean of four equally sized10-task suites. Distinct from LeRobot native-format1970/2000=98.5% and MuJoCo3.3.2 evaluation.

Publisher evaluation · Reported; not independently reproduced

LaWAM original paper v2 · LIBERO Spatial ↗

Success rate: 99.4 %

Original LaWAM paper v2 developer-reported research results, version-scoped rather than assigned to a downloaded artifact or LeRobot conversion. Protocols and latency overhead exclusions remain explicit. Table1/AppendixF.2:40LIBERO tasks,50trials/task,2000trials total after benchmark-specific25k-step post-training. Mean of four equally sized10-task suites. Distinct from LeRobot native-format1970/2000=98.5% and MuJoCo3.3.2 evaluation.

Publisher evaluation · Reported; not independently reproduced

LaWAM original paper v2 · LIBERO Average ↗

Success rate: 98.6 %

Original LaWAM paper v2 developer-reported research results, version-scoped rather than assigned to a downloaded artifact or LeRobot conversion. Protocols and latency overhead exclusions remain explicit. Table1/AppendixF.2:40LIBERO tasks,50trials/task,2000trials total after benchmark-specific25k-step post-training. Mean of four equally sized10-task suites. Distinct from LeRobot native-format1970/2000=98.5% and MuJoCo3.3.2 evaluation.

Publisher evaluation · Reported; not independently reproduced

LaWAM original paper v2 · RoboTwin Clean ↗

Success rate: 92.64 %

Original LaWAM paper v2 developer-reported research results, version-scoped rather than assigned to a downloaded artifact or LeRobot conversion. Protocols and latency overhead exclusions remain explicit. Table2/AppendixF.3:50RoboTwin2.0 tasks,100trials/task per clean/randomized condition,5000trials/condition. Post-trained100ksteps on2500clean+25000randomized demonstrations. Overall91.22 is the equally weighted mean of clean92.64 and randomized89.80.

Publisher evaluation · Reported; not independently reproduced

LaWAM original paper v2 · RoboTwin Randomized ↗

Success rate: 89.8 %

Original LaWAM paper v2 developer-reported research results, version-scoped rather than assigned to a downloaded artifact or LeRobot conversion. Protocols and latency overhead exclusions remain explicit. Table2/AppendixF.3:50RoboTwin2.0 tasks,100trials/task per clean/randomized condition,5000trials/condition. Post-trained100ksteps on2500clean+25000randomized demonstrations. Overall91.22 is the equally weighted mean of clean92.64 and randomized89.80.

Publisher evaluation · Reported; not independently reproduced

LaWAM original paper v2 · RoboTwin Overall ↗

Success rate: 91.22 %

Original LaWAM paper v2 developer-reported research results, version-scoped rather than assigned to a downloaded artifact or LeRobot conversion. Protocols and latency overhead exclusions remain explicit. Table2/AppendixF.3:50RoboTwin2.0 tasks,100trials/task per clean/randomized condition,5000trials/condition. Post-trained100ksteps on2500clean+25000randomized demonstrations. Overall91.22 is the equally weighted mean of clean92.64 and randomized89.80.

Publisher evaluation · Reported; not independently reproduced

LaWAM original paper v2 · A100 model-only chunk latency ↗

Model-only latency: 187 ms

Original LaWAM paper v2 developer-reported research results, version-scoped rather than assigned to a downloaded artifact or LeRobot conversion. Protocols and latency overhead exclusions remain explicit. AppendixF.1:mean1000CUDA-synchronized model-only action-chunk predictions onA100,BF16,10action-expert denoising steps. Excludes simulator/robot execution; each baseline retains native resolution/horizon/workload.187ms is not robot control frequency or universal end-to-end latency;24x refers to compared pixel-space WAMs.

Technical details

Functional purpose

Action control / Latent future prediction

Prediction output

Latent visual subgoals and action chunks

Model card

Research on dynamics-aware manipulation with Qwen3-VL-2B, frozen DINOv3 features, a latent world model and an action expert. Original packages include a LAM component, pretraining policy and separate LIBERO/RoboTwin post-trained policies. June 14 is the first research disclosure; October 25 is paper v2, not a verified new model or weight release date. Public native .pt artifacts are distinct from LeRobot safetensors conversions. Publisher model cards declare MIT, while DINOv3 is separately gated under its own license; no unrestricted end-to-end commercial right is inferred. The native LIBERO config has use_state=false despite padded state dimensions. Paper LIBERO98.6% is not the separate LeRobot98.5% result. Reported187ms is model-only A100/BF16/10-action-denoising-step latency, excluding robot/simulator execution. Camera motion and fine deformable dynamics remain limitations; hosted API is unverified. [Paper](https://arxiv.org/abs/2606.15768) · [Code](https://github.com/RLinf/LaWAM)

Before you use this version: Public native .pt artifacts are distinct from LeRobot safetensors conversions. Publisher model cards declare MIT, while DINOv3 is separately gated under its own license; no unrestricted end-to-end commercial right is inferred. The native LIBERO config has use_state=false despite padded state dimensions. Paper LIBERO98.6% is not the separate LeRobot98.5% result. Reported187ms is model-only A100/BF16/10-action-denoising-step latency, excluding robot/simulator execution. Camera motion and fine deformable dynamics remain limitations; hosted API is unverified. Available weights or code do not establish plug-and-play robot compatibility. Check the required configuration, data and interfaces in the source documentation.

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