arXiv AI
Jul 28
VLASH: Real-Time VLAs via Future-State-Aware Asynchronous Inference
arXiv:2512. 01031v2 Announce Type: replace-cross Abstract: Vision-Language-Action models (VLAs) are becoming increasingly capable across diverse robotic tasks.
Asynchronous Robot Inference: Decoupling Action Prediction and Execution
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arXiv Machine Learning
Jun 9
C$^3$ache: Accelerating World Action Models with Cross Inference Chunk Cache
arXiv:2606. 08962v1 Announce Type: new Abstract: World Action Models (WAMs) generalize better than standard Vision-Language-Action (VLA) policies to novel motions and environments, because a video-modeling objective lets them learn from abundant unlabeled video rather than scarce labeled robot demonstrations.
arXiv Machine Learning
Jul 31
CLAM: Continuous Latent Action Models for Robot Learning from Unlabeled Demonstrations
arXiv:2505. 04999v2 Announce Type: replace-cross Abstract: Learning robot control policies from demonstrations typically requires action-labeled expert data, which is expensive to collect through teleoperation.
Hugging Face Trending Papers
Jul 7
Training-Free Acceleration for Vision-Language-Action Models with Action Caching and Refinement
Vision-Language-Action (VLA) models have emerged as a promising approach for generalizable robotic manipulations. In particular, flow matching-based VLA models have shown remarkable success due to their capability to generate precise and smooth action sequences and capture multimodal distributions.
arXiv AI
Aug 17
Reflex: Enabling Fast and Predictive Vision-Language-Action Models for Reaction-Critical Manipulation
arXiv:2608. 14379v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have recently achieved promising performance in robotic manipulation.
arXiv AI
Jul 14
IntentVLA: Short-Horizon Intent Modeling for Aliased Robot Manipulation
arXiv:2605. 14712v2 Announce Type: replace-cross Abstract: Robot imitation data are often multimodal: similar visual-language observations may be followed by different action chunks because human demonstrators act with different short-horizon intents, task phases, or recent context.
arXiv AI
Jun 4
DEFLECT: Temporal Counterfactual Preference Learning for Delay-Robust Asynchronous VLAs
arXiv:2605. 19294v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) policies increasingly rely on asynchronous inference to hide large-model latency behind ongoing robot motion.
arXiv Machine Learning
Jun 3
SeeTraceAct: Visibility-Aware Latent Planning from Cross-Embodiment Demonstration Videos
arXiv:2606. 02745v1 Announce Type: cross Abstract: Vision-language-action models (VLAs) are promising general-purpose robot policies, but adapting them to new tasks typically requires costly task-specific teleoperation data.
arXiv AI
Aug 18
Algorithm-Architecture Co-Design for Efficient VLA Inference via Speculative Inference and Verification
arXiv:2608. 15636v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have demonstrated remarkable capabilities in the field of embodied AI, but their high computational cost and limited predicted action length hinder real-time deployment.
Hugging Face Trending Papers
2d ago
Act with Intent: Distilling Behavior Intent for Vision-Language-Action Models
Vision-Language-Action (VLA) models can turn multimodal context into robot actions, but their action decoders are still trained largely by behavior cloning. This supervises which motor command was dem...
arXiv AI
Jun 26
WatchAct: A Benchmark for Behavior-Grounded Robot Manipulation
arXiv:2606. 26443v1 Announce Type: cross Abstract: A robot working alongside people must reason about what they have done, in what order, and with what intent.
arXiv AI
1d ago
Act with Intent: Distilling Behavior Intent for Vision-Language-Action Models
The paper introduces Intention Distillation (INDI), a method that injects behavior-level intent into Vision‑Language‑Action (VLA) model decoders by leveraging a frozen teacher vision‑language model to interpret demonstrations. During training, the teacher processes the current observation, instruction, coarse action summary, and execution video, producing a multimodal intent representation that the VLA decoder uses alongside trajectory and execution features to predict actions. Experiments on SimplerEnv‑Bridge, RoboCasa Kitchen, and real‑world tasks show that INDI consistently improves success rates, especially on longer‑horizon tasks, demonstrating that explicit modeling of semantic intent benefits action decoders.