Papers

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.

Dexterous ManipulationVLA

Ruijie Zheng, Dantong Niu, Yuqi Xie · 198 views · Feb 18, 2026

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.

VLATest-Time Adaptation

Hongxin Zhang, Chunru Lin, Tsun-Hsuan Wang · 191 views · Sep 24, 2026

DONIC: Generalist Dexterous Bimanual Tracking Control from Human Demonstrations

Despite recent progress in dexterous reference tracking, existing models focus primarily on single-hand manipulation and a limited range of interactions, leaving the bimanual dexterous behaviors common in real life unaddressed. The scarcity of contact-rich bimanual manipulation data and the increased complexity of coordinating contacts make developing a generalist bimanual tracker particularly challenging. Therefore, we present 1) DONIC, the first generalist bimanual tracker jointly trained across operation categories and human-motion sources, together with 2) BiHOI, a multi-view dataset of 1,575 sequences spanning 229 objects and emphasizing sustained bimanual interaction, where reconstructed interactions are retargeted into robot references. DONIC combines proximal policy optimization with behavioral cloning in a feedback loop that recycles verified successful executions into training demonstrations. An object-centered per-hand reward maintains object-motion feedback while supervising each hand according to its reference contact role. Clip-Centered Advantages remove reference-dependent advantage offsets while preserving within-trajectory differences. Together, these designs enable one single policy to learn coordinated manipulation across heterogeneous references. Without finetuning, a single checkpoint achieves 86.79% success on unseen trajectories of training objects and 57.32% on held-out objects, surpassing the state-of-the-art bimanual tracking baselines by 77.36 and 45.73 percentage points.

Dexterous ManipulationBimanual Manipulation

Mingze Sun, Ziang Cao, Yuan Fei · 241 views · Oct 21, 2026

T-WLA: Bridging Slow Vision and Fast Touch

T-WLA shares a history-aware visual memory between a slow vision-language-action policy and a fast tactile residual editor. The visual branch produces an action chunk and memory at 39.7 Hz; the lightweight tactile editor reuses them to correct only unexecuted actions at 119.1 Hz. It improves tactile-conditioned hand-motion prediction and contact-rich manipulation, reaching 50.0% average success on four real-robot tasks with 30 demonstrations each.

Tactile SensingVisuotactile Manipulation

Kaichen Zhou, Yuzhen Chen, Hannah Yao · 309 views · Oct 20, 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.

Embodied AIRobot Manipulation

Galbot Team, Chen, Xuchuan, Cheng, Xiaoqian · 571 views · Sep 29, 2026

Temporal Visuo-Tactile Learning for Dexterous Grasp Stability

Most robotic grasping research picks a pose from vision and closes a parallel gripper, while humans rely on fingertip touch and almost never drop things. The LASR Lab at TU Dresden reframes the question as "is this multi-fingered grasp stable before we lift?" and answers it with data. They mount four Digit 360 sensors on the fingertips of a 16-DoF Tilburg Hand on a 7-DoF xArm7 and automatically collect 10,000 grasp trials over 200 objects (105 rigid, 95 deformable, 1.9-244 g), recording external RGB-D, proprioception and four tactile streams (camera, audio, IMU, pressure) throughout each grasp. Stability labels come from SAM 3 measuring post-lift object height and agree with manual verification on 92.34% of trials. The predictor consumes a 3-second window anchored at grasp initiation: 16-frame RGB and tactile clips go through pretrained VideoMAEv2-Base, 100-step proprioception and 224-step audio/IMU/pressure go through Transformers, fingertip features are fused across modalities, time and fingers by weight-shared factorized attention, and everything is aggregated by attention fusion into a post-lift stability probability. Under 5-fold object-disjoint cross-validation, vision+proprioception+touch reaches 83.55% and removing touch costs 3.99 points (79.56%). Tactile spatial resolution matters monotonically (79.40% at 1x1 up to 83.55% at 112x112), and freezing touch to its final frame costs 2.07 points, so the useful signal is high-resolution and dynamic. Deployed as an online lift-or-regrasp gate (threshold 0.95, up to five regrasps) on 20 objects excluded from training, the visuo-tactile gate reaches 82.0% success among executed lifts, 10.5 points above a non-tactile gate. The ~1.2 TB dataset is public on OPARA.

Dexterous ManipulationTactile Sensing

Ken Nakahara, Aleksei Buvailik, Prokhor Kotov · 651 views · Oct 7, 2026

DexTacWAM: A Visuo-Tactile World-Action Model for Dexterous Manipulation

Dexterous manipulation depends on contact dynamics that are often only partially observable from vision. Recent World-Action Models (WAMs) couple predictive video world modeling with action generation, but remain largely vision-centric and therefore cannot directly model these contact dynamics. We present DexTacWAM, a visuo-tactile WAM that encodes each fingertip independently, aggregates the resulting features through a finger- and pose-aware tactile compressor, and injects the tactile latent into a video diffusion world model for joint visuo-tactile world modeling. Across six contact-rich dexterous manipulation tasks on a 22-DoF bimanual platform, DexTacWAM achieves the highest score on every task, averaging 70.6 versus 38.0 for the strongest baseline. Ablations attribute the gain to modeling contact evolution as part of the predicted world state rather than tactile conditioning alone: removing tactile world modeling reduces the four-task mean from 74.7 to 26.6 while keeping the same tactile features and action expert. After four hours of tactile-encoder adaptation with a frozen pretrained vision VAE, our continual vision-to-touch learning extends the pretrained video model to touch using roughly 100 demonstrations per task without tactile midtraining, while retaining visual prediction quality within 0.5 dB of vision-only counterparts. The compressor retains 89.4% of pre-fusion contact recall while enabling 2.26x faster training and 1.29x faster inference. Together, these results show that pretrained video priors can be extended to distributed multi-finger contact dynamics in a data- and compute-efficient manner.

Dexterous ManipulationWorld-action models

Haoran Yuan, Zekai Wang, Boning Shao · 888 views · Sep 21, 2026

Towards Human-level Dexterous Teleoperation

Humans reorient, translate, and regrasp objects within a single hand by orchestrating continuous contact transitions, yet existing dexterous teleoperation either retargets kinematics while ignoring contact forces and object inertia, or relies on generative action priors that drift in closed loop. TeleDexter formulates dexterous teleoperation as hand-object co-tracking: the operator specifies synchronized subgoal sequences of fingertip positions and object pose, and a single-stage reinforcement-learning controller learns in simulated contact how to realize them. A hybrid reward couples sparse subgoal reaching with dense tracking, geometry-aware two-stage retargeting converts mocap human hand-object data into physically feasible reference motions, and random action masking plus domain randomization enable zero-shot sim-to-real transfer. On seven reorientation and long-horizon tool-use tasks with a Franka FR3 and SharpaWave or LeapHand hands, TeleDexter averages 75.2% success and 87.1% task progress where baselines nearly uniformly fail, and 50 demonstrations per task behavior-clone into autonomous policies reaching 46.7-73.3% success.

Dexterous ManipulationTeleoperation

Li, Puhao, Chen, Zeyuan, Wu, Yingying · 841 views · Jul 13, 2026

N₀-Foundation: Towards the Age of Tactile Intelligence

We present N₀-Foundation, a paradigm for tactile-enabled embodied manipulation, which integrates tactile sensing hardware, large-scale multimodal data, tactile representation learning, and standardized evaluation. First, we engineer the infrastructure for scalable data collection, including a vision-based tactile sensor, a tactile Universal Manipulation Interface (UMI), and a synchronized visuo-tactile data collection system supporting both robot embodiments and UMI-based demonstrations. Leveraging this infrastructure, we construct NeoData, which contains more than 30000 hours of synchronized visual and tactile demonstrations, spanning six embodiments, 450 tasks, and billions of paired RGB and tactile frames collected through a mixture of real-robot teleoperation and UMI-based demonstrations. To facilitate open research, we further release OpenNeoData, a 5000-hour open-source subset of NeoData. The dataset addresses a central limitation of existing manipulation corpora, critical for deformable-object manipulation, precise assembly, delicate force control, and sustained surface interaction. Capitalizing on the large-scale, heterogeneous tactile measurements, we propose NeoForce, a visuo-tactile representation model that learn transferable tactile representations across different sensor designs. To enable systematic evaluation of tactile embodied models built upon our infrastructure, datasets and tactile representations, we further propose a comprehensive benchmark, which combines the real-world NeoReal suite and the simulated NeoSim suite for standardized evaluation. Experiments across both suites show that policies benefit from the physical contact state rather than from the device-specific appearance of the tactile signal. We release the dataset, the representation, and the benchmark, aiming at supporting future work on tactile-enabled embodied manipulation.

UMIVLA

NeoteAI Team, Fudan TEAI Team · 1.2k views · Aug 30, 2026

Pre-training Visual Dexterity in Simulation

Large-scale pre-training has made robot policy fine-tuning increasingly data-efficient, but this progress has largely been driven by datasets and embodiments built around simple parallel-jaw grippers. Dexterous, multi-fingered hands remain comparatively data-starved because real teleoperation is costly to scale, while human hand video is off-embodiment and requires lossy pose estimation and retargeting. We introduce Simulation Pre-training for Dexterity (SPD), a pre-training framework for dexterous manipulation that uses data entirely collected in simulation. In SPD, humans manipulate virtual objects inside a VR headset, enabling on-embodiment trajectories and robot-free collection. With the help of five operators, we collect 75 hours of multi-task dexterous manipulation over one week, and use it to pre-train a causal transformer on a sequence modeling objective. We study the benefits of simulation pre-training on real-world tasks by fine-tuning on 1-2 hours of physical demonstrations on a 56-DoF bimanual dexterous setup. We find that our approach outperforms training behavior cloning policies from scratch, showing that simulation teleoperation is a viable pre-training source for real-world dexterous manipulation. We perform ablation studies, measuring the benefits of history conditioning and short action chunks for reactive control.

Sim-to-RealDexterous Manipulation

Sarthak Kamat, Adam Rashid, Satvik Sharma · 835 views · Aug 16, 2026

G0.5: One Autoregressive Stream for Robot Reasoning and Action

G0.5 is a pretrained autoregressive VLA in which a single transformer decoder emits reasoning and action tokens under one objective. A cross-embodiment action tokenizer, a native chain-of-thought stream, and a visual memory module make this tractable at foundation-model scale, letting the pretrained VLM capabilities carry over to physical behavior. It surpasses pi-0.5 and GR00T-N1.7 across 7 regimes including real-world R1 robot fine-tuning, BEHAVIOR Challenge, DROID zero-shot transfer, LIBERO, RoboTwin 2.0 and SimplerEnv.

VLAGalaxea

Yicheng Liu, Zibin Dong, Baijun Ye · 898 views · Aug 12, 2026

Cross-Embodiment Robot Manipulation via a Unified Hand Action Space

Robot manipulation policies are typically tied to specific robotic hand embodiments, limiting the transfer of learned behaviors across platforms with different kinematic structures. In this work, we propose the Unified Hand Action Space (UHAS), a sphere-based unified action representation for cross-embodiment dexterous manipulation. UHAS represents robotic hand actions as geometric deformations of a canonical sphere and uses a Cascade Inverse Kinematics (CIK) algorithm to map the shared representation to embodiment-specific joint configurations. Using reinforcement learning, we train dexterous manipulation policies directly in the proposed action space for in-hand cube reorientation tasks. We evaluate our method in both simulation and real-world experiments across multiple robotic hands, including the Allegro Hand, LEAP Hand, Shadow Hand, and MANO Human Hand. Experimental results demonstrate effective dexterous manipulation, zero-shot transfer to unseen hands, rapid finetuning across embodiments, and successful real-world deployment.

Dexterous ManipulationCross-Embodiment

Luis Felipe Casas, Robert Teal, Keval Shah · 531 views · Jul 3, 2026

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