#Robot Manipulation (71)

What Visual Representations Do World Action Models Need?

World Action Models inherit their visual foundation from video-generation VAEs trained for pixel reconstruction, which leaves manipulation-relevant information underrepresented. This study holds one Wan2.2 dual-expert backbone and one training protocol fixed and compares 13 frozen visual representations, 5 feature-integration mechanisms and multiple feature compositions on LIBERO-Plus. Three findings emerge. First, general-purpose representations beat specialized ones on average (82.2 vs 80.0 overall success), and every perturbation family is topped by a general source. Second, channel-wise joint prediction is the strongest mechanism (81.5 mean over 13 sources, 5.1 points ahead of the runner-up), and its gain comes mainly from predicting future features rather than from conditioning on the current frame. Third, extra sources help only when complementary: the largest additional gain is 3.7 points, and pairing a distilled encoder with its own teacher (C-RADIOv4 + DINOv3) drops success to 78.6. The resulting recipe R²-WAM (DINOv3 + UniTok + V-JEPA 2.1, channel-wise) reaches 84.7% on RoboCasa GR1 Tabletop (+5.9) and 92.8% on RoboTwin 2.0 (+2.2), and improves all four Galbot G1 real-robot tasks by roughly 10 points.

Kai Liu, Jiangran Lyu, Tingyu Deng · 36 views · Oct 9, 2026

World-action modelsVisual RepresentationRobot ManipulationOct 9, 2026

VT-Bridge: Bridging Pretrained Foundation VLAs to VTLAs via Lightweight Residual Adaptation

Vision-Tactile-Language-Action (VTLA) models have demonstrated clear advantages over Vision-Language-Action (VLA) models in contact-rich manipulation. However, developing VTLA models is severely constrained by the massive amounts of vision-tactile data and computational resources required. To address this bottleneck, we propose VT-Bridge, a lightweight residual adaptation strategy that bridges pretrained foundation VLAs to VTLAs. Rather than training a VTLA model from scratch or modifying the original architecture of a pretrained VLA, VT-Bridge employs an identical lightweight residual-adapter architecture across VLA backbones and uses backbone-specific weights to refine actions at the robot execution frequency. This design substantially lowers the data and training barriers. Specifically, it requires up to 50 vision-tactile demonstrations per task to fine-tune a VLA backbone and train a 0.98M-parameter residual adapter. Experiments with three representative VLA backbones (π₀, π_0.5, and SmolVLA) across four contact-rich manipulation tasks further demonstrate its consistent effectiveness across VLA architectures. On average, VT-Bridge raises the task completion rate from 11.7% with task-level VLA fine-tuning alone to 62.9%. Together, these findings demonstrate the broad applicability, effectiveness, and accessibility of VT-Bridge for contact-rich manipulation. The project page is available at https://hoxnocha.github.io/vt-bridge-web/.

Yansong Wu, Tuo Yang, Rongping Zhao · 42 views · Sep 18, 2026

Tactile SensingVLAContact-Rich ManipulationSep 18, 2026

Systematically Exploring the Capabilities of GPT-6 Astra as Embodied Policies

Evaluates GPT-6 Astra as an embodied policy across six domains: hybrid control reaches 48% on RoboDojo and 92% on RxR navigation, while direct in-hand control and dense locomotion references stay unreliable; token use and inference latency constrain practical control.

Galbot Team, Chen, Xuchuan, Cheng, Xiaoqian · 61 views · Sep 29, 2026

Embodied AIRobot ManipulationDexterous ManipulationSep 29, 2026

0:57

LeWAM: An End-to-End JEPA That Controls Real Robots

@Minghao__Fu · 171 views · 2026-10-09

World ModelsJEPAManipulation

Beyond Data Scaling: Representation-Centric Continued Pre-training for Vision-Language-Action Models

Scaling robot data is crucial for building generalist Vision-Language-Action (VLA) models, yet robot trajectories are harder to scale than web-scale image-text data because embodied collection is costly and sparsely covers the physical world. This makes representation quality a central bottleneck: under a fixed robot-data budget, continued pre-training must turn limited trajectories into transferable visual-action knowledge rather than merely fit actions. We propose VLAct, a VLA-oriented VLM backbone trained on broad, heterogeneous, multi-embodiment robot data before task-specific fine-tuning. VLAct preserves the broad VLM prior and encourages shared action semantics across embodiments through VLM-prior preservation, multi-head continuous action co-supervision, and a partially unified cross-embodiment action layout, while allowing task-specific action heads during fine-tuning. Across simulation, real-world, and unseen-embodiment transfer, VLAct consistently improves downstream performance under fixed fine-tuning protocols. On LIBERO-Plus and RoboTwin 2.0, VLAct surpasses industrial VLA systems including ABot-M0 and LingBot-VLA, achieving success rates of 82.6% and 92.5%. On RoboDojo, VLAct ranks sixth among all policies by success rate and outperforms all explicitly designated world-action model (WAM) entries on both metrics. Most notably, on RoboCasa-GR1, an unseen humanoid embodiment, VLAct using only 20% of downstream trajectories outperforms the full-data GR00T-N1.6 baseline. These results are obtained using fully open-source data and only a 16-GPU training setup, showing that representation-centric continued pre-training can deliver highly competitive performance under a modest compute budget and is an important independent axis of VLA progress beyond data scaling.

Senqiao Yang, Chengyao Wang, Yuxin Chen · 242 views · Aug 27, 2026

VLAContinued Pre-trainingCross-EmbodimentAug 27, 2026

Efficient-WAM: A 1B-Parameter World-Action Model with Low-Cost Future Imagination

World-Action Models (WAMs) have emerged as a promising paradigm for embodied control by coupling future visual prediction with action generation. However, many existing WAMs rely on photorealistic future prediction, which incurs high inference latency and makes real-time robot deployment difficult. We introduce Efficient-WAM, a World-Action Model motivated by the idea that a compact future branch can still support effective action generation, even at lower visual fidelity. Efficient-WAM improves inference efficiency via a compact video expert transferred from WAN-2.2-5B, token-sparse video latents, and asymmetric video-action denoising that allocates fewer sampling steps to video than to actions. Instead of prioritizing visual fidelity, Efficient-WAM treats future video prediction as a compact guidance signal for action generation. Experiments on RoboTwin 2.0 and real-world manipulation tasks show that Efficient-WAM maintains strong action performance despite visibly coarse future predictions. Our 1B-parameter model achieves 98 ms per action chunk during physical deployment, over 30x faster than the Motus baseline with comparable task success.

Jiajun Li, Tiecheng Guo, Yifan Ye · 206 views · Jun 8, 2026

World-action modelsRobot ManipulationFlow MatchingJun 8, 2026

RoboPrompt: Intuitive Robot Policy Steering with Sparse Human Input

End-to-end robot policies trained through imitation learning remain constrained by limited data diversity, making reliable zero-shot deployment in real-world settings challenging. Shared-autonomy methods enable human correction through teleoperation, but specialized hardware and operator training hinder deployment at scale. Other approaches incorporate human guidance as additional policy inputs, often requiring architectural changes and dedicated training for steerability, which limits their applicability across policies. We present RoboPrompt, a general-purpose, lightweight robot policy steering system that enables users to guide policy behavior through intuitive, sparse inputs, including drawn traces, target points, and coarse directional instructions. RoboPrompt decouples human-intention translation from the underlying policy: a reusable module converts human guidance into action drafts, which are refined through the diffusion or flow-matching dynamics of the base policy. By controlling action generation in noise space, RoboPrompt balances human intent with the policy prior without modifying the base policy architecture or fine-tuning it for steerability. Experiments demonstrate effective steering across Diffusion Policy, π_0.5, and FastWAM. We further use steered rollouts for online policy improvement through DAgger. After 2-3 rounds of iteration, average success rates increase by 15.5% for π_0.5 across three tasks and by 21.3% across three policies(Diffusion Policy, π_0.5, FastWAM) on the Insert Bread task, while average human intervention counts decrease by 44.0% (2.86 to 1.60) and 81.9% (2.60 to 0.47), respectively.

Yanwen Zou, Chenyang Shi, Guoxuan Xu · 278 views · Oct 7, 2026

ManipulationImitation LearningDiffusion policiesOct 7, 2026

SkeleWAM: squeezing a manipulation scene into a sparse 3D skeleton

World action models usually represent future state as video or visual latents, which encode geometry only implicitly and carry appearance irrelevant to control. SkeleWAM keeps just three kinds of 3D landmarks - robot joints, object centres and interaction points - and uses that sparse skeleton for both action generation and future-skeleton prediction, training with the latter as auxiliary supervision and dropping it at inference. On LIBERO-Plus it reaches 85.9% zero-shot success with 57.1M parameters, beating the 2B-parameter Cosmos-Policy by 3.7 points.

Juyi Sheng, Hua Wang, Mengyuan Liu · 402 views · Oct 1, 2026

VLAWorldActionModelManipulationOct 1, 2026

2:41

Mind-1: Human-Speed Physical AI for Real Robot Work

@QingxuZhu · 516 views · 2026-09-30

Physical AIRobot ManipulationHumanoid

World Action Agent: Harnessing VLMs for Robot Manipulation via World Action Rehearsal

HKUST(GZ), CUHK and Knowin AI present World Action Agent (WAA), a multi-agent harness that changes what the VLM sees and how its decisions take effect — rather than the VLM itself — so a general-purpose VLM can pilot a robot with basic tools (point, drag, preview, execute), making every decision inside one visual action workspace. Three properties: Contact views, whose camera parameters are solved from interaction-region visibility under occlusion, framing compactness, view redundancy and stability, with the feasible set constraining the two views to orthogonal horizontal projections so any alignment error can be read along two independent directions — requiring only a base-frame point cloud, so the perception source (simulation, fused RGB-D, or VGGT reconstruction) is irrelevant; action rehearsal, where each action is an editable proposal planned by cuRobo, overlaid as a translucent robot in every view with a feasibility report, refined by an Imagination Agent in a separate context while the physical scene stays unchanged, and only proposals with executable plans become motion; and in-view correction, where the agent drags from a reference point to the desired location in the Contact view where the error is observed — the reference can be a visible point on the held object, so no conversion into an absolute gripper pose is needed. Skills evolve from expert videos and human Canvas-GUI teaching through Learner, Editor and Reviewer roles under evidence-citing and independent-review constraints. On LIBERO-Pro with Gemini 3.7 Flash, using skills evolved only from LIBERO-90 and frozen before evaluation, WAA reaches a state-of-the-art 75.6% average success, above ASPIRE (72.0%), end-to-end VLAs and the same-backbone Show-Harness (6.7%), scoring 80.0 / 73.3 on the two Spatial splits; an episode costs just 31 model calls, 150 s and $0.1996 versus 120 calls, 874 s and $0.5021 for Show-Harness. The frozen skills transfer to robosuite at 100.0%, and LoRA fine-tuning Qwen3.5-9B on 112 trajectories (1,774 decision steps) as the main agent only raises out-of-domain success from 1.7% to 43.3%.

Yehang Zhang, Haojian Huang, Yifan Chang · 776 views · Sep 24, 2026

World Action AgentWAAVLMSep 24, 2026

0:28

HIL-UMI: Bringing Human-in-the-Loop Post-Training of Vision-Language-Action Models to Universal Manipulation Interface

Large-scale vision-language-action (VLA) models provide powerful priors for robot manipulation, yet adapting them to a specific deployment remains challenging. Supervised fine-tuning (SFT) on task-specific demonstrations provides a step toward deployment, but faces two persistent limitations: static data provide limited coverage of out-of-distribution states, and standard imitation objectives do not distinguish progressing behavior from less useful data. Interactive post-training can address these limitations, but typically requires repeated policy execution and human intervention on a physical robot. We introduce HIL-UMI, a policy-guided Universal Manipulation Interface (UMI) framework for robot-free human-in-the-loop VLA post-training. During handheld UMI demonstrations, HIL-UMI queries the current policy on the same observation stream without executing its predictions. The Energy Score compares the human action trajectory with policy inference and triggers collection when their discrepancy indicates an out-of-distribution region. In a separate feedback loop, low online advantage predictions identify essential segments for refining a progress-based advantage estimator. The updated estimator then guides advantage-conditioned behavioral cloning using a balanced mixture of base demonstrations and new policy data. This design preserves the iterative and policy-aware nature of human-in-the-loop learning while decoupling data collection from robot deployment. Experiments on four real-world tasks spanning long-horizon and precise manipulation show that HIL-UMI achieves consistent improvement over SFT and benefits from both targeted collection and advantage refinement. Moreover, HIL-UMI outperforms HG-DAgger on Clean Up Table with lower per-frame collection time, suggesting a scalable path for VLA post-training across operators and locations.

Han, Zimu, Zeng, Yiming, Zhang, Jiyao · 1k views · Sep 17, 2026

VLAHuman-in-the-LoopPost-trainingSep 17, 2026

0:39

Opus 5 fails a simulated toxic-lab safety task

@ariansadeg · 686 views · 2026-09-19

Physical AI SafetyVLARobotic Manipulation

1:02

TRON 2 VLA Demo: Autonomous Drawer Opening and Toy Retrieval

@LimX_Dynamics · 577 views · 2026-09-18

VLADual-arm robotsManipulation

1:08

DIDO: One-Step Denoising for World Action Models

@stepjamUK · 712 views · 2026-09-15

World-action modelsRobot ManipulationVideo Generation

Show-Harness: Just a VLM Agent Can Play Robots

Foundation vision-language models (VLMs) exhibit broad intelligence about the world, yet translating this intelligence into robot control remains challenging. We present Show-Harness, an Embodied Harness that enables VLMs to "play" robots through a compact semantic interface linking intent to action. Show-Harness exposes discrete semantic action units that VLMs can naturally reason over, while embodiment-specific interpreters deterministically ground them into local robot actions, keeping the VLM directly responsible for fine-grained physical decisions. Through the same interface, Show-Harness demonstrates the feasibility of (1) directly unlocking closed-source frontier VLMs for zero-shot robot control, and (2) adapting small-scale open-source VLMs for low-cost deployment with just a few GPU-hours of fine-tuning. We further develop GUMI (GUI Manipulation Interface), which extends the same semantic action space to GUI-based demonstration collection, allowing humans and agents to "play" robots across embodiments without specialized teleoperation hardware. Extensive experiments show that Show-Harness-equipped VLM agents generalize robustly across tasks, embodiments, and environments, outperforming representative agentic and VLA paradigms. These results suggest that the right interface can unlock substantial embodied capability from foundation VLMs, without requiring additional model capacity or costly embodiment-specific pretraining.

Yanzhe Chen, Zechen Bai, Zhijun Cao · 1k views · Sep 9, 2026

Vision-language modelsAgentic HarnessSemantic Action InterfaceSep 9, 2026

As an Amazon Associate, we earn from qualifying purchases.

#Robot Manipulation (71)

What Visual Representations Do World Action Models Need?

VT-Bridge: Bridging Pretrained Foundation VLAs to VTLAs via Lightweight Residual Adaptation

Systematically Exploring the Capabilities of GPT-6 Astra as Embodied Policies

LeWAM: An End-to-End JEPA That Controls Real Robots

Beyond Data Scaling: Representation-Centric Continued Pre-training for Vision-Language-Action Models

Efficient-WAM: A 1B-Parameter World-Action Model with Low-Cost Future Imagination

RoboPrompt: Intuitive Robot Policy Steering with Sparse Human Input

SkeleWAM: squeezing a manipulation scene into a sparse 3D skeleton

Mind-1: Human-Speed Physical AI for Real Robot Work

World Action Agent: Harnessing VLMs for Robot Manipulation via World Action Rehearsal

Awesome-UMI Adds 9 New Device Projects

Proxy Policy Steering: Test-Time Robot Policy Adaptation

Real-Time EXPO-FT: Reliable VLA Fine-Tuning

Fisheye-VLA: One Fisheye Camera Beats Head + Wrist Cameras

PragmaBot: Learning to Plan Tasks by Experiencing the Real World

PiPER recovers after moving-target tests

GPT-6 Astra Tops Independent Robot Manipulation Eval at 70.5%

Real-Time EXPO-FT: Fast, Reliable RL for Real-Time VLA Policies

Splat-MOVER: A Robot That Updates Its 3D World After Every Move

HIL-UMI: Bringing Human-in-the-Loop Post-Training of Vision-Language-Action Models to Universal Manipulation Interface

Opus 5 fails a simulated toxic-lab safety task

TRON 2 VLA Demo: Autonomous Drawer Opening and Toy Retrieval

DIDO: One-Step Denoising for World Action Models

Show-Harness: Just a VLM Agent Can Play Robots