#VLA (208)

A one-stop VLA toolbox from Dexmal: the unified DexData format spans pretraining, fine-tuning (full/LoRA/RL), inference, and evaluation, supporting mainstream models (π0, CogACT, OFT, MemVLA, GR00T N1) plus the in-house dual-expert DM0; DB-pretraining brings consistent gains across five simulation benchmarks, with 62% average success on RoboChallenge Table30 real-robot evaluation.

VLAManipulation

Python1.4k1814

Democratizing Robot-Use Agents: A Unified Harness with Open Models

Robot-use Agents (RUAs) have shown promising zero-shot manipulation capabilities, but reliance on frontier, proprietary models limits research into further training. To democratize RUA research, we introduce uni-robot-agent, a unified harness for competitive open-weight robot use that supports further training. Without privileged inputs, an out-of-the-box Qwen3.8-Flash-Next achieves a RoboDojo Score of 31.39, surpassing the published GPT-6 Astra-RoboProbe result of 28.97 and many VLA/WAM baselines, and performs competitively on 19 real-world tasks. Our analysis across model generations, families, and sizes links effective robot use to four general capabilities, suggesting that future RUA training should develop them jointly.

Jiarui Yuan, Yuxin Zuo, Xiaoyu Huang · 27 views · Nov 1, 2026

Robot-Use AgentsOpen models

Nov 1, 2026

Real2Sem2Real: 3D Semantic Augmentations for Robot Learning from a Single Human RGB Demonstration

Human video scales easily and does not need robot hardware, but existing methods require depth sensing, overlay the robot onto 2D video, or generate data only for the demonstrated objects. We present Real2Sem2Real (R2Sem2R), a real-to-semantic-to-real pipeline that turns one monocular RGB video of a human demonstration into thousands of robot demonstrations across the semantic object instances of a category. R2Sem2R tracks the SE(3) pose of each object with DINOv3-distilled Gaussian splats, outperforming existing methods and FoundationPose with estimated metric depth. R2Sem2R then aligns digital cousins, each generated from one or a few photographs, to the demonstrated objects with a semantic alignment. This alignment matches whole objects and the functional regions of target objects, such as a coffee machine's tray. R2Sem2R maps the human hand to a feasible robot grasp that extends to the cousins. Robot demonstrations are generated either in IsaacSim, for photorealistic rendering, or MuJoCo, for contact physics, and R2Sem2R can generate over 2,000 episodes in about two hours. On two real robots and four tasks, we train a vision-language-action policy on data from one human video per task, and policies trained on our data match or exceed policies trained on teleoperation across diverse object instances. The project page is available at https://real2sem2real.github.io/.

Matthew Strong, Ankush K. Dhawan, Julien Poffet · 46 views · Oct 20, 2026

Robot LearningHuman demonstrations

Oct 20, 2026

0:26

Galaxea G0.5 VLA Joins LeRobot

@LeRobotHF · 17 views · 2026-11-03

VLAHumanoid

0:21

Zero-Shot Mobile Manipulation on Dexmate

@youliangtan · 16 views · 2026-11-03

Mobile ManipulationVLA

RoboFoundry: System-as-Policy Evolution for Self-Learning Embodied Agents

RoboFoundry freezes the foundation model and treats the surrounding context, skill, and semantic-binding system as the evolving policy, allowing embodied agents to diagnose failures, validate persistent repairs, and transfer improvements across benchmarks and robots.

Jingsong Liang, Shuhao Liao, Shizhe Zhang · 68 views · Sep 26, 2026

Embodied AgentSystem-as-Policy

Sep 26, 2026

0:31

EgoScale: Scaling Dexterous Manipulation with Diverse Egocentric Human Data

EgoScale is a human-to-dexterous-manipulation transfer framework. A VLA policy is pretrained on 20,854 hours of action-labeled egocentric human video, revealing a log-linear relationship between data scale and validation loss. Lightweight aligned human-robot mid-training then grounds the learned motor prior in robot control, improving average success by 54% over a no-pretraining baseline on a 22-DoF dexterous hand and transferring to lower-DoF hands.

Ruijie Zheng, Dantong Niu, Yuqi Xie · 146 views · Feb 18, 2026

Dexterous ManipulationVLA

Feb 18, 2026

Empirical Results Fine-Tuning pi0.5 on a Real Manufacturing Task

Dream Machines fine-tunes pi0.5 on a bimanual actuator unboxing task: 76% with 21h of data, 90% after one clean hour, 93% from RTC serving settings alone, while LoRA and training from scratch fall far short.

BLOG

VLAFine-tuning

Self-Adaptive VLA for Robust Robot Deployment

This post-training method turns a policy's own shifted rollouts into latent context, uses a lightweight encoder and adaptive layer normalization to adapt a vision-language-action policy, and recovers over 80% of base performance across four precision-critical manipulation tasks.

Hongxin Zhang, Chunru Lin, Tsun-Hsuan Wang · 155 views · Sep 24, 2026

VLATest-Time Adaptation

Sep 24, 2026

PACE: Temporal-Role Tactile Conditioning for Chunk-Based Insertion Policies

Contact-rich insertion requires a robot to combine visual alignment with tactile adjustment. Existing visuotactile chunk-based policies often collapse tactile feedback into one generation-time pathway, so persistent contact state, abrupt contact events, and the future contact consequences of an action are modeled implicitly in a single representation. PACE assigns one tactile stream three temporal roles. An exponential-smoothed contact state modulates action-chunk generation through layer-wise feature conditioning. A short-window tactile difference forms an event cue and drives a gated residual correction over only the first steps of the chunk. A training-only head predicts future tactile increments from decoder states as an auxiliary representation-learning objective. This preserves chunk-level action continuity while giving slowly varying contact phases and fast collision or jamming events separate pathways. Across five real insertion tasks, PACE reaches 90.7 percent average success, more than 44 percentage points above vision-only and single-path tactile-fusion baselines. It also averages 88.7 percent on six ManipulationNet geometry and clearance settings, including 0.1 mm clearance. Ablations show complementary contributions from Phase, Event, and Pred., while cross-signal and cross-architecture tests extend the principle to force/torque, joint current, and diffusion-policy backbones.

Zhiyuan Chen, Shixiong Xu, Shuai Chen · 242 views · Oct 12, 2026

Tactile SensingRobot Manipulation

Oct 12, 2026

2:52

OneWM: World and Action Models That Strengthen Each Other

@Selen7005717917 · 146 views · 2026-10-24

World ModelsRobot Manipulation

Can World and Action Modeling Benefit Each Other under Unified Causal Training?

World and action modeling are closely related, yet their reciprocal benefits under unified causal training remain unclear. We introduce OneWM, a simple framework for unified causal training that treats actions as both conditions for world prediction and targets for policy learning within a shared video-action architecture. Using this framework, we investigate whether learning forward dynamics conditioned on specified actions improves action learning, and whether learning to generate actions in turn improves action-conditioned world modeling. We use LIBERO, LIBERO-Plus, and RoboCasa365 as embodied testbeds to evaluate both policy learning and world modeling, complemented by real-world policy experiments. Learning the visual consequences of specified actions improves policy performance and generalization across changing scenes. In turn, action learning improves multi-view world-prediction fidelity under the same architecture. Together, these results answer both questions positively, supporting reciprocal benefits between world modeling and action generation under unified causal training.

Yue Su, Bokai Lin, Xinkai Wang · 198 views · Oct 15, 2026

World ModelsRobot Manipulation

Oct 15, 2026

Training Generalist Navigation Policies via Action Self-Distillation

Action Self-Distillation turns future trajectory feedback into an inferred language goal, then trains one open-source vision-language model to predict reasoning and ego-frame paths from onboard observations without human language or reasoning labels.

Bowen Feng, Keqin Wang, Yulong Yang · 272 views · Oct 13, 2026

NavigationVLA

Oct 13, 2026

1:00

Action Self-Distillation: One VLM Trains Itself Into a Generalist Navigation Policy

@BowenFengbf · 153 views · 2026-10-22

Self-DistillationVLA

Thinker-WM: A Unified World Model with Shared Multimodal Representations for Embodied Intelligence

World models have emerged as a promising paradigm for embodied AI, yet existing systems often remain fragmented in the modalities they model and the functions they support. In particular, visual dynamics, geometric structure, and executable action are commonly coupled through separate interfaces or downstream modules rather than being jointly represented and evolved within a shared dynamics model. In this work, we present Thinker-WM, a unified multimodal world model that jointly models and generates RGB, metric depth, and robot action through a shared intermediate Unified Dynamics Transformer with modality-specific encoders and decoders. The model performs multimodal unification at the representation level: appearance, geometry, and action are transformed into a common dynamic representation and jointly evolved by shared parameters, while their distinct output structures are preserved. The same parameterization supports multiple functions, including joint video-action generation, world prediction, policy inference, action-conditioned generation, video-to-action generation, and inverse dynamics. To examine whether the shared representation captures action-related information, we introduce a lightweight motion probe that shows the frozen visual representation contains recoverable motion information. Thinker-WM is trained on a heterogeneous corpus comprising approximately 20K hours of egocentric video, 10K hours of real-robot data, and 1K hours of simulation data, including approximately 4K hours of real-robot data with aligned depth supervision. Extensive experiments demonstrate strong performance across embodied world-model evaluations. Thinker-WM ranks first on EWMBench, WorldOlympiad and WorldArena, while controlled studies further demonstrate the benefits of action conditioning, geometric interaction, and multi-function training within the shared representation.

Thinker Team, Peng Gao, Jichao Jiao · 264 views · Oct 13, 2026

World ModelsEmbodied AI

Oct 13, 2026