Good Papers

Showing Vision-language-action models Show all papers

71%Highly rated

RealtimeWAM: One-Step Asynchronous World Action Models

RealtimeWAM uses teacher-anchored consistency distillation and cross-expert wavefront pipelining for one-step asynchronous action generation, achieving near-lossless performance with ~25x speedup.

Chengtao Lv, Jinyang Du, Shuyi Feng, Yang Yong and 6 more

Published Oct 5, 2026 · ▲ 12 on Hugging Face · Code ★ 2,880

0% Readers0 of 1 upvoted
14/20 AI panelreviewers recommend it

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 14 of 20 reviewers recommend it
lenient 4/5
medium 9/10
strict 1/5
69%Highly rated
?Highly ratedVote to see the score

Arm-wise Compositional Generalization in Dual-Arm Vision-Language-Action Models

ACG-Bench evaluates arm-wise compositional generalization in dual-arm vision-language-action models via AE-VLA, which achieves 21.53% simulated and 39% real-world success versus under 6% baselines.

Zaibin Zhang, Binghao Ran, Yuhan Wu, Zhongbo Zhang and 9 more

Published Oct 5, 2026 · ▲ 4 on Hugging Face

0% Readers0 of 1 upvoted
13/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 13 of 20 reviewers recommend it
lenient 4/5
medium 7/10
strict 2/5
88%Must read
?Must readVote to see the score

When to Switch: Reliable Action-Chunk Extension for Vision-Language-Action Models

RACE predicts subskill transition timing to extend VLA action chunks reliably, reducing stop-and-go idle time ~5x on real robots while improving success rates.

Seonghoon Yu, Dongwon Kim, HyungRok Jung, Yoonjae Baek and 3 more

Published Oct 5, 2026 · ▲ 13 on Hugging Face · Code ★ 3

– ReadersNo votes yet
15/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 15 of 20 reviewers recommend it
lenient 5/5
medium 9/10
strict 1/5
88%Must read
?Must readVote to see the score

A Safe Action Is Not Enough: Feasible-Future Decoding for Vision-Language-Action Policies

Feasible-future decoding reranks VLA actions by future safe-completion mass, reducing cumulative safety costs by up to 57.5% without retraining or rollouts.

Tu Nguyen, Matthieu Zimmer, Vu Anh Vu, Ziyi Wang and 3 more

Published Oct 4, 2026 · ▲ 3 on Hugging Face

– ReadersNo votes yet
15/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 15 of 20 reviewers recommend it
lenient 4/5
medium 10/10
strict 1/5
83%Must read
?Must readVote to see the score

DiVeR: Decision-Critical Verifier Learning for VLA Test-Time Scaling

DiVeR improves verifier-guided VLA test-time scaling by reweighting learning toward decision-critical states using action representation dispersion, boosting success without extra annotations or overhead.

Seongheon Park, Heecheol Kim, Shulin Tian, Lilika Makabe and 4 more

Published Oct 4, 2026 · ▲ 1 on Hugging Face

– ReadersNo votes yet
13/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 13 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 0/5
83%Must read

PerturBot: Breaking Shortcut Priors in Vision-Language-Action Models with Perturbative Training

PerturbBot breaks vision-language-action shortcut priors via perturbative training while GroundingFscore diagnoses shortcut reliance, enabling healthier scaling without altering inference.

Mingyu Liu, Chonghao Sima, Tianjian Feng, Hanqing Wang and 3 more

Published Oct 3, 2026 · ▲ 13 on Hugging Face · Code ★ 6

– ReadersNo votes yet
13/20 AI panelreviewers recommend it

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 13 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 1/5
80%Must read

Native Action-Prior Learning from Videos for World Action Models

NAVA-WAM pretrains robot action policies directly from observation-only videos via flow-matching and joint attention, improving control accuracy and label efficiency.

Zhaochong An, Fei Zhang, Menglin Jia, Duncan Frost and 9 more

Published Oct 2, 2026 · 0 citations · ▲ 81 on Hugging Face

– ReadersNo votes yet
12/20 AI panelreviewers recommend it

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 12 of 20 reviewers recommend it
lenient 5/5
medium 6/10
strict 1/5
78%Highly rated
?Highly ratedVote to see the score

Kinematic MeanFlow: One-Step Action Generation Policy for Robotic Foundation Models

Kinematic MeanFlow decouples MeanFlow's time derivative via a kinematic identity to stabilize one-step robotic action generation, cutting latency by up to 74% while outperforming multi-step flow matching.

Jiawei Fan, Sifeng Wang, Yuqing Hou, Anbang Yao

Published Oct 1, 2026 · 0 citations · Code

– ReadersNo votes yet
11/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 11 of 20 reviewers recommend it
lenient 3/5
medium 8/10
strict 0/5
89%Must read

World Action Modeling with Progressive Visual Planning

ProWAM predicts sparse visual sub-goals and actions via progressive planning, achieving state-of-the-art long-horizon robotic control and strong zero-shot real-world generalization.

Fei Zhang, Zhaochong An, Duncan Frost, Yikai Wang and 4 more

Published Oct 1, 2026 · 0 citations · ▲ 83 on Hugging Face

– ReadersNo votes yet
16/20 AI panelreviewers recommend it

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 16 of 20 reviewers recommend it
lenient 5/5
medium 9/10
strict 2/5
88%Must read
?Must readVote to see the score

Learning from Runtime Feedback through Failure-Bank Self-Evolution for Vision-Language-Action Models

FailBank turns runtime shield feedback into persistent VLA policy updates via failure-bank self-evolution, raising success rates up to 25.4 points and cutting policy-induced cost up to 35.6%.

Mingyue Cui, Zheyuan Liu, Yihan Zhu, Zheyuan Zhang and 1 more

Published Sep 30, 2026 · 0 citations · ▲ 15 on Hugging Face · Code ★ 2

– ReadersNo votes yet
15/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 15 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 2/5
80%Must read
?Must readVote to see the score

Toward Real-Time VLAs: Stage-Aware Two-Step Flow Denoising and System-Level Evaluation

Two-step non-uniform flow denoising reduces VLA inference time from 61.6 ms to 22 ms by exploiting early-stage velocity stability. A distributed real-time framework and garment-folding evaluation show joint model-system optimization preserves task success with lower latency.

Di Wu, Rongtian Shen, Ping Liu, Yan Shen and 7 more

Published Sep 30, 2026 · 0 citations · Code ★ 1

– ReadersNo votes yet
12/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 12 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 0/5
88%Must read
?Must readVote to see the score

Taming VLAs under Robot Execution Errors: Self-Compensation and Stress Testing

Self-compensating VLA adapts online to robot execution errors via residual feedback, improving success over 30 points on physical arms and outperforming training-time robustness methods on RoboStress.

Sohyun Lee, Yoonjae Baek, Jaesang Won, Jinnyeong Kim and 4 more

Published Sep 29, 2026 · 0 citations · ▲ 18 on Hugging Face

100% Readers1 of 1 upvoted
14/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 14 of 20 reviewers recommend it
lenient 5/5
medium 6/10
strict 3/5
83%Must read
?Must readVote to see the score

Beyond Future Prediction: Denoising as Generative Adaptation for Robot Control

NowWAM adapts pretrained diffusion transformers to robot control via current-observation denoising and action prediction, achieving 87.7% on LIBERO-Plus with halved tokens and 1.8x speedup.

Zanyi Wang, Yuheng Lei, Dengyang Jiang, Ping Luo and 3 more

Published Sep 23, 2026 · 0 citations · ▲ 25 on Hugging Face · Code ★ 8

– ReadersNo votes yet
13/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 13 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 1/5
89%Must read
?Must readVote to see the score

MolmoAct2: Action Reasoning Models for Real-world Deployment

MolmoAct2 is an open vision-language-action model with a specialized reasoning backbone, open action tokenizer, continuous-action expert, and adaptive reasoning that outperforms closed and open baselines across embodied reasoning and robot deployment benchmarks.

Haoquan Fang, Jiafei Duan, Donovan Clay, Sam Wang and 25 more

Published May 4, 2026 · 0 citations · ▲ 357 on Hugging Face · Code ★ 794

– ReadersNo votes yet
16/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 16 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 3/5
71%Highly rated
?Highly ratedVote to see the score

Green-VLA: Staged Vision-Language-Action Model for Generalist Robots

Green-VLA stages vision-language-action training across five curriculum levels to generalize across robot embodiments. It uses scaled demonstration processing, embodiment-aware actions, and RL alignment to improve real-world humanoid success rates and long-horizon efficiency.

I. Apanasevich, M. Artemyev, R. Babakyan, P. Fedotova and 24 more

Published Jan 31, 2026 · 0 citations · ▲ 323 on Hugging Face · Code ★ 141

– ReadersNo votes yet
7/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 7 of 20 reviewers recommend it
lenient 4/5
medium 3/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score

Lift, See, Act: Hierarchical Robot Policy Pretraining with 3D Foundation Models

Yiyuan Ge, Changxing Ding, Ziyu Hao, Zijie Zheng and 1 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score

Learning a Trajectory-Geometric Condition from Reasoning for VLA Planning

Yuguang Yang, Zhewen Tan, Canyu Chen, Cheng Chi and 8 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score

TGPO: Trace-Guided Policy Optimization for Robot Task Planning via Verifiable Subgoal Generation

Zhihong Liu, Yang Li, RenMing Huang, Chendong Zeng and 2 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
67%Highly rated
?Highly ratedVote to see the score

Coarse-to-Refine: Trajectory Self-Refinement in Single Autoregressive Pass for Driving VLA

Canyu Chen, Yuguang Yang, Jianing Pang, Zhewen Tan and 8 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
2/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 2 of 20 reviewers recommend it
lenient 2/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score

Long-term Embodied Visual Tracking with Lightweight Vision-Language-Action Models

Haowei Sun, Kaining Chen, Xutao Wen, Xinze Xie and 4 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
Show 20 more papers