arXiv AI
inr2action: Encoding Robot Episodes as Neural Implicit Representations for Recall and Action
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LVMem: Image-Augmented Memory for Long-Horizon Question Answering
arXiv:2610.27104v1 Announce Type: new Abstract: Reliable language-guided perception is essential for embodied agents, where questions about objects, events, and their locations must be answered from...
arXiv Computer Vision
Oct 12
Dual-Stream Memory for Vision-Language-Action Policies
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 AI
Jul 1
MIRTH: Mutual-Information Reasoning with Temporal Hubs for Vision-Language-Action Agents
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 Computer Vision
Oct 15
Robota-Embed: Unified VLA Embeddings for Robot In-Context Learning and Evolution
arXiv:2610.19495v1 Announce Type: cross Abstract: Retrieving useful context for a robot requires matching its task, execution stage, and the continuation that should follow. We introduce Robota-Embed...
arXiv AI
Oct 15
Memory Without Curation: Learning Multi-Stream Latent Memory for Robot Policies
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...
arXiv Computer Vision
Oct 2
Compressing History into Memory: Distilling Transformers into Recurrent Transformers
arXiv:2606.21562v2 Announce Type: replace Abstract: Transformers are AI's workhorse but their computational cost becomes prohibitive when processing long sequences. We target long-horizon streaming v...