arXiv:2610.14406v1 Announce Type: cross Abstract: It is widely appreciated that robotic policies require long-term memory to perform complex tasks. However, directly conditioning policies on all past...
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:2608. 04765v1 Announce Type: cross Abstract: Vision-language-action (VLA) models provide a unified paradigm for connecting visual perception, language understanding, and robotic control.
arXiv:2606.20092v3 Announce Type: replace Abstract: Memory remains a critical bottleneck for long-horizon robotic manipulation, as standard Vision-Language-Action (VLA) policies often fail when task-...
arXiv:2603. 04910v2 Announce Type: replace-cross Abstract: Imitation learning from human demonstrations has achieved significant success in robotic control, yet most visuomotor policies still condition on single-step observations or short-context histories, making them struggle with non-Markovian tasks that require long-term memory.
AWARE is a plug‑in module for Vision‑Language‑Action models that retrieves observation evidence without extra training. It tracks uncertainty via Action Entropy, uses associative memory to fetch relevant features, and weights them with instruction‑guided attention, scaling the injection by entropy and depth. Experiments on LIBERO, SIMPLER, CALVIN, and real‑world robot tasks show consistent performance gains, especially on long‑horizon tasks, with negligible computational cost.
CondenseVLA introduces a learnable history‑condensation module that distills short‑term visual context into a fixed set of slot tokens via learnable queries and cross‑attention, reducing redundancy and improving action decoding. The approach achieves notable gains on several robot manipulation benchmarks, raising Simpler‑WidowX success from 58.7% to 69.8%, reaching 97.2% on LIBERO, 52.8% on real‑world tasks, and 21.6% on memory‑dependent RMBench tasks.
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:2609.32453v2 Announce Type: replace-cross Abstract: Robotic manipulation is inherently history-dependent, yet most pretrained robotic policies condition on only the current observation or a sho...
HiFOR introduces a hierarchical foresight framework for Vision‑Language‑Action models, combining short‑term state evolution, semantic milestones, and final goal predictions via a Hierarchical Dependency Mask. It structures future latent representations across horizon, view, and spatial dimensions, enabling fine‑grained retrieval. An action‑guided factorized retrieval mechanism then selectively pulls relevant future information during denoising to guide action generation, improving robotic manipulation performance across simulation and real‑world tasks.
arXiv:2511. 18960v4 Announce Type: replace Abstract: Vision-Language-Action (VLA) models have shown remarkable progress in embodied tasks recently, but most methods process visual observations independently at each timestep.
The paper introduces Divide-and-Remember (D&R), a recursive memory method for vision-language-action (VLA) policies that optimises memory by maximizing the conditional mutual information between actions and memory given observations. D&R recursively divides the full history into top‑K selections over 2K tokens, using a shared lightweight selector across all recursion blocks to handle unbounded histories efficiently. Evaluated on the RoboMME benchmark of 16 long‑horizon manipulation tasks, D&R achieves state‑of‑the‑art success rates with consistent gains across all suites while using only 64 tokens, and similar improvements are observed in real‑robot experiments.