Mix-QVLA is a task-evidence-aware mixed-precision PTQ framework for VLA models that preserves task-relevant evidence via evidence-mass and attribution-distribution metrics to guide bit allocation under memory and BitOps constraints.
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UniVLA: Learning to Act Anywhere with Task-centric Latent Actions
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abstract
A generalist robot should perform effectively across various environments. However, most existing approaches heavily rely on scaling action-annotated data to enhance their capabilities. Consequently, they are often limited to single physical specification and struggle to learn transferable knowledge across different embodiments and environments. To confront these limitations, we propose UniVLA, a new framework for learning cross-embodiment vision-language-action (VLA) policies. Our key innovation is to derive task-centric action representations from videos with a latent action model. This enables us to exploit extensive data across a wide spectrum of embodiments and perspectives. To mitigate the effect of task-irrelevant dynamics, we incorporate language instructions and establish a latent action model within the DINO feature space. Learned from internet-scale videos, the generalist policy can be deployed to various robots through efficient latent action decoding. We obtain state-of-the-art results across multiple manipulation and navigation benchmarks, as well as real-robot deployments. UniVLA achieves superior performance over OpenVLA with less than 1/20 of pretraining compute and 1/10 of downstream data. Continuous performance improvements are observed as heterogeneous data, even including human videos, are incorporated into the training pipeline. The results underscore UniVLA's potential to facilitate scalable and efficient robot policy learning.
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- abstract A generalist robot should perform effectively across various environments. However, most existing approaches heavily rely on scaling action-annotated data to enhance their capabilities. Consequently, they are often limited to single physical specification and struggle to learn transferable knowledge across different embodiments and environments. To confront these limitations, we propose UniVLA, a new framework for learning cross-embodiment vision-language-action (VLA) policies. Our key innovation is to derive task-centric action representations from videos with a latent action model. This enab
- background generative modeling in pixel space, while transformer-based architectures like ACT [50] and Perceiver-Actor [36] leverage spatiotemporal attention for long-horizon manipulation. In paral- lel, recent 2D VLA models further extend visuomotor learning to multimodal settings. Systems such as OpenVLA [21], OpenVLA-OFT [20],π 0 [3], RT-2 [56], RT-X [29], RoboFlamingo [25], Octo [38], GR-1 [41], and UniVLA [4] integrate large vision-language backbones with robot ac- tion policies, enabling semantic gro
- method a latent space 𝑍∈R 𝑇×𝐶×𝐻×𝑊 , where 𝑇 , 𝐶, 𝐻, and 𝑊 denote the number of frames, channel, height, respectively. Unlike video/image VAEs whose primary goal is compression, our approach targets semantic understanding by leveraging a self-supervised backbone with rich high-level representations. Specifically, we adopt DI- NOv2 [39], which is trained with both contrastive learning [ 8] and masked image modeling [57]. Causally-Constrained Framewise Autoregression.Videos pos- sess an intrinsic temporal
- baseline Table 1: Benchmark comparison on multiple embodied manipulation tasks. CALVIN denotes "ABCD→D" and CALVIN∗denotes "ABC→D", LIBERO-plus∗denotes finetuning with LIBERO-plus dataset Model Size LIBERO LIBERO-plus LIBERO-plus ∗RoboCasa-50 GR1 CALVIN CALVIN∗Robotwin2 # VLA π0 [4] 3B 94.4 53.6 - 42.4 - - 3.92 65.9/58.4 π0-FAST[111] 3B 85.5 61.6 - - - - - - X-VLA [112] 0.9B - - - - - 4.43 - 72.9/72.8 UniVLA [87] 8B 95.5 - - - - 4.63 4.41 - gr00t-N1.6 [5] 3B 93.9 - - 36.0 47.6 4.60 4.24 - π0.5 [40] 3B 96
- baseline actionless videos using latent actions. RLA achieves the highest average success rate and rank. Suc- cess rates are evaluated over 50 episodes (seeds 42-91) and averaged over the last five checkpoints. Method PushT Roll Pull Pull Tool Poke Rank # Avg SR " BC-ResNet 3.6 42.0 33.6 7.6 49.2 3.8 27.2 DINO CLS [ 39 ] 7.6 39.6 40.4 4.4 44.8 4.0 27.4 UniVLA [ 44 ] 6.0 37.6 42.8 7.2 50.0 3.8 28.7 AdaWorld [ 24 ] 9.2 38.4 48.4 10.8 61.6 2.2 33.7 RLA (Ours) 15.2 43.8 43.6 12.0 63.6 1.2 35.6 4.2 Minimalist
- background The second paradigm treats humans as an alternative embodiment, either by jointly training on human and robot data or by aligning behaviors in a shared latent action space [6,35,72,82]. Despite reducing some representationgaps,thesemethodsstillfaceasubstantialcross-embodimentgap. The third paradigm leverages human data for general visual representation or predictive world-model pretraining [11,46,73]. However, these methods mainly focus onwhatactions are executed, they largely overlookwhya parti
- background by explicitly validating the dataset through a large-scale, re- producible study designed to ensure robot-learning readiness. Robot Learning from Human Data.Human data presents two main opportunities for robot learning: abundant unlabeled online videos and curated, labeled demonstrations [1, 29, 46, 52]. Web videos, though plentiful, require pseudo-labeling of actions via inverse dynamics models [6, 13, 55], affor- dances [2, 44], or point tracking [4, 42, 50] for policy training, forming a basi
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representative citing papers
PearlVLA achieves SOTA on LIBERO by separating VLM representations into visual grounding and an iterative latent plan branch refined via world model queries and RefineNet with process-reward RL.
FTP-1 is the first foundation tactile policy pretrained on ~3000 hours of data from 26 sources across 21 sensors that improves performance on seen setups by 17.2% and transfers to unseen sensors with 31% success rate gain.
ActProbe is an action-space detector that uses temporal consistency error and action chunk magnitude from policy outputs, mapped via LSTM-MLP, to predict failures earlier than baselines across policies and real-robot tasks.
A hypernetwork generates complete task-specific visuomotor policy parameters from instructions alone to structurally eliminate observation leakage in language-conditioned robotic control.
RotVLA models latent actions as continuous SO(n) rotations with triplet-frame supervision and flow-matching to reach 98.2% success on LIBERO and 89.6%/88.5% on RoboTwin2.0 using a 1.7B-parameter model.
Reducing visual input to one token per frame in VLA world models maintains or improves long-horizon performance on MetaWorld, LIBERO, and real-robot tasks.
RLA-WM predicts residual latent actions via flow matching to create visual feature world models that outperform prior feature-based and diffusion approaches while enabling offline video-based robot RL.
Being-H0.7 adds future-aware latent reasoning to direct VLA policies via dual-branch alignment on latent queries, matching world-model benefits at VLA efficiency.
CF-VLA uses a coarse initialization over endpoint velocity followed by single-step refinement to achieve strong performance with low inference steps on CALVIN, LIBERO, and real-robot tasks.
Mask World Model predicts semantic mask dynamics with video diffusion and integrates it with a diffusion policy head, outperforming RGB world models on LIBERO and RLBench while showing better real-world generalization and texture robustness.
GeCO replaces time-dependent flow matching with time-unconditional optimization, enabling adaptive inference and intrinsic OOD detection for robotic imitation learning.
UniLACT improves VLA models by adding depth-aware unified latent action pretraining that outperforms RGB-only baselines on seen and unseen manipulation tasks.
PhysGen uses video models to learn physics for robots, outperforming baselines by up to 13.8% on Libero and matching specialized models in real-world tasks.
DreamGen trains robot policies on synthetic trajectories from adapted video world models, enabling a humanoid robot to perform 22 new behaviors in seen and unseen environments from a single pick-and-place teleoperation dataset.
VLAFlow shows that combining language-supervised co-training with future latent alignment produces the most stable transfer performance for vision-language-action models trained on mixed robot data.
Introduces H-Tac human tactile-action dataset and TTP pre-training that unifies spaces and predicts future tactile signals to improve robotic dexterous manipulation transfer.
SA-VLA adds state conditioning to VQ-based action tokenization in VLA policies, expanding each discrete token's effective support to state-dependent actions and raising average success rates from 0.29 to 0.56 on 12 sim tasks and 0.15 to 0.33 on 3 real tasks.
T^2VLA is a test-time reinforcement learning framework for VLAs that uses internal confidence to define intrinsic rewards via similarity to high-confidence expert demonstrations and a dual-expert bootstrapping mechanism.
WatchAct is a new benchmark of 3000 instances across 14 tasks in four cognitive domains for evaluating video-grounded robot manipulation, with current systems achieving at most 16.3% success.
OpenHLM is an empirical recipe yielding a whole-body humanoid VLA model that outperforms GR00T N1.6 and Ψ0 baselines on long-horizon tasks using less than half the demonstration time.
GLAM learns a shared latent action space grounded in consistent future observation prediction across heterogeneous data sources to train improved behavioral cloning policies for robot manipulation tasks.
PoLAR imposes radial structure on latent actions in hyperbolic space to factorize extent and mode, improving robot policy performance over baselines.
Tri-Info uses three information theory signals on action diversity, temporal consistency, and state coupling to predict VLA model failures with cross-domain generalization to 83% real-world accuracy.
citing papers explorer
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Mask World Model: Predicting What Matters for Robust Robot Policy Learning
Mask World Model predicts semantic mask dynamics with video diffusion and integrates it with a diffusion policy head, outperforming RGB world models on LIBERO and RLBench while showing better real-world generalization and texture robustness.
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OFlow: Injecting Object-Aware Temporal Flow Matching for Robust Robotic Manipulation
OFlow unifies temporal foresight and object-aware reasoning inside a shared latent space via flow matching to improve VLA robustness in robotic manipulation under distribution shifts.
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World Action Models are Zero-shot Policies
DreamZero uses a 14B video diffusion model as a World Action Model to achieve over 2x better zero-shot generalization on real robots than state-of-the-art VLAs, real-time 7Hz closed-loop control, and cross-embodiment transfer with 10-30 minutes of data.