arXiv subjects

Yuyin Yang

Publications and source records attributed to Yuyin Yang.

4 recordsLinked to original sources

arXiv2026

InternVLA-A1.5: Unifying Understanding, Latent Foresight, and Action for Compositional Generalization

Unified models for robot manipulation aim to equip one policy with both the semantic priors of pretrained VLMs and the physical dynamics learned through future prediction. In practice, existing designs tend to erode the semantics of the pretrained backbone, suffer interference among heterogeneous objectives, and learn future prediction from scratch in pixel space, leaving the dynamics priors of pretrained video generators unexploited. We present InternVLA-A1.5, which builds the policy on a native VLM backbone that keeps training on VQA and subtask prediction, and attaches a lightweight unified expert for continuous action generation. Future prediction is recast as a latent-querying problem, where a small set of learnable foresight tokens condenses the task-relevant future into a compact latent code under the supervision of a frozen pretrained video generation model, so the policy inherits world-model dynamics priors without ever learning pixel-level generation. The video branch is discarded at inference, keeping real-time control. Pretrained on 1.2M robot episodes and 3M multimodal samples, InternVLA-A1.5 achieves the best overall results on all six simulation benchmarks. In the real world, the preserved semantics deliver the strongest compositional generalization on held-out instruction bindings, and the two designs together sustain long-horizon execution.

cs.RO↗

arXiv2026

InternVLA-A1: Unifying Understanding, Generation and Action for Robotic Manipulation

Prevalent Vision-Language-Action (VLA) models are typically built upon Multimodal Large Language Models (MLLMs) and demonstrate exceptional proficiency in semantic understanding, but they inherently lack the capability to deduce physical world dynamics. Consequently, recent approaches have shifted toward World Models, typically formulated via video prediction; however, these methods often suffer from a lack of semantic grounding and exhibit brittleness in the presence of video prediction errors. To synergize semantic understanding with dynamic predictive capabilities, we present InternVLA-A1. This model employs a unified Mixture-of-Transformers architecture, coordinating three experts for scene understanding, visual foresight generation, and action execution. These components interact seamlessly through a unified masked self attention mechanism. Building upon InternVL3 and Qwen3-VL, we instantiate InternVLA-A1 at 2B and 3B parameter scales. We pre-train these models on heterogeneous data sources over real-world robot data, synthetic simulation data, and human videos, covering over 692M frames. This hybrid training strategy effectively harnesses the diversity of synthetic simulation data while minimizing the sim-to-real gap. We evaluated InternVLA-A1 on 12 real-world robotic tasks and a simulation benchmark. The results show that InternVLA-A1 consistently outperforms prior leading models: compared with pi0.5, it achieves +4.4\% on static manipulation tasks and +2.6\% on the RoboTwin 2.0 simulation benchmark, and delivers a +26.7\% boost on dynamic manipulation tasks.

cs.RO↗

arXiv2025

InternData-A1: Pioneering High-Fidelity Synthetic Data for Pre-training Generalist Policy

Recent works explore how real and synthetic data contribute to Vision-Language-Action (VLA) models' generalization. While current VLA models have shown the strong effectiveness of large-scale real-robot pre-training, synthetic data has not previously demonstrated comparable capability at scale. This paper provides the first evidence that synthetic data alone can match the performance of the strongest $\pi$-dataset in pre-training a VLA model, revealing the substantial value of large-scale simulation. The resulting model also exhibits surprisingly zero-shot sim-to-real transfer on several challenging tasks. Our synthetic dataset, InternData-A1, contains over 630k trajectories and 7,433 hours across 4 embodiments, 18 skills, 70 tasks, and 227 scenes, covering rigid, articulated, deformable, and fluid-object manipulation. It is generated through a highly autonomous, fully decoupled, and compositional simulation pipeline that enables long-horizon skill composition, flexible task assembly, and heterogeneous embodiments with minimal manual tuning. Using the same architecture as $\pi_0$, we pre-train a model entirely on InternData-A1 and find that it matches the official $\pi_0$ across 49 simulation tasks, 5 real-world tasks, and 4 long-horizon dexterous tasks. We release the dataset and will open-source the generation pipeline to broaden access to large-scale robotic data and to lower the barrier to scalable data creation for embodied AI research.

cs.RO↗

arXiv2025

Gripper Keypose and Object Pointflow as Interfaces for Bimanual Robotic Manipulation

Bimanual manipulation is a challenging yet crucial robotic capability, demanding precise spatial localization and versatile motion trajectories, which pose significant challenges to existing approaches. Existing approaches fall into two categories: keyframe-based strategies, which predict gripper poses in keyframes and execute them via motion planners, and continuous control methods, which estimate actions sequentially at each timestep. The keyframe-based method lacks inter-frame supervision, struggling to perform consistently or execute curved motions, while the continuous method suffers from weaker spatial perception. To address these issues, this paper introduces an end-to-end framework PPI (keyPose and Pointflow Interface), which integrates the prediction of target gripper poses and object pointflow with the continuous actions estimation. These interfaces enable the model to effectively attend to the target manipulation area, while the overall framework guides diverse and collision-free trajectories. By combining interface predictions with continuous actions estimation, PPI demonstrates superior performance in diverse bimanual manipulation tasks, providing enhanced spatial localization and satisfying flexibility in handling movement restrictions. In extensive evaluations, PPI significantly outperforms prior methods in both simulated and real-world experiments, achieving state-of-the-art performance with a +16.1% improvement on the RLBench2 simulation benchmark and an average of +27.5% gain across four challenging real-world tasks. Notably, PPI exhibits strong stability, high precision, and remarkable generalization capabilities in real-world scenarios. Project page: https://yuyinyang3y.github.io/PPI/

cs.RO↗