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StreamPI introduces a streaming multimodal temporal modeling framework that enhances Vision‑Language‑Action models by adding temporal reasoning without extra parameters. It anchors each visual observation and language instruction pair as a temporal unit, using bidirectional attention for cross‑modal fusion and causal attention for autoregressive streaming inference. The method employs random‑interval streaming training to improve robustness and leverages the LLM backbone’s length extrapolation to inherit pretrained weights, achieving superior performance over pi0.5 on real‑robot and simulation tasks.
The paper introduces a dual‑stream memory architecture for vision‑language‑action policies, separating a task‑agnostic temporal compressor from a task‑conditioned visual retriever. The temporal compressor uses a Gated DeltaNet to condense history into a single token, while the retriever keeps frames above a relevance threshold and fuses them into spatial tokens. Evaluated on sixteen RoboMME tasks and four real‑robot tasks, the combined model outperforms the FrameSamp+Modul baseline, achieving higher mean success rates and notable gains on tasks requiring execution‑stage discrimination.
arXiv:2609.16864v1 Announce Type: cross Abstract: Vision-language-action (VLA) models have achieved impressive performance in quasi-static manipulation, but struggle in dynamic manipulation tasks bec...
arXiv:2606. 31167v1 Announce Type: cross Abstract: VLA models have emerged as a powerful paradigm for transferring semantic knowledge from web-scale data to physical robotic control.
arXiv:2608. 04765v1 Announce Type: cross Abstract: Vision-language-action (VLA) models provide a unified paradigm for connecting visual perception, language understanding, and robotic control.
Zero-shot cross-task generalization, where a policy must execute manipulation tasks never seen during training, remains a central challenge in robot learning. In large language models, a novel task ca...