MemER: Scaling Up Memory for Robot Control via Experience Retrieval
arxiv.org · 11,129 words · saved by 1 readers
N/A
# link_2b0qm5mk3zu.pdf ## Metadata - PDFFormatVersion=1.7 - IsLinearized=false - IsAcroFormPresent=false - IsXFAPresent=false - IsCollectionPresent=false - IsSignaturesPresent=false - Author=Ajay Sridhar; Jennifer Pan; Satvik Sharma; Chelsea Finn - Creator=arXiv GenPDF (tex2pdf:e76afa9) - Custom.DOI=https://doi.org/10.48550/arXiv.2510.20328 - Custom.License=http://creativecommons.org/licenses/by/4.0/ - Custom.PTEX.Fullbanner=This is pdfTeX, Version 3.141592653-2.6-1.40.28 (TeX Live 2025) kpathsea version 6.4.1 - Custom.arXivID=https://arxiv.org/abs/2510.20328v1 - Producer=pikepdf 8.15.1 - Titl
saved by
related reading
- π*0.6: a VLA That Learns From Experiencephysicalintelligence.company
- How Claude Performs on Robotics Tasks \ Anthropicanthropic.com
- Explore | alphaXivalphaxiv.org
- A VLA with Open-World Generalizationpi.website
- State of Robot Learning, December 2025vedder.io
- Causal Video Models Are Data-Efficient Robot Policy Learners | Rhoda AIrhoda.ai
- Steerable Vision-Language-Action Policies for Embodied Reasoning and Hierarchical Controlsteerable-policies.github.io
- Helix: A Vision-Language-Action Model for Generalist Humanoid Controlfigure.ai
- Precise Manipulation with Efficient Online RLpi.website
- RoboTTT: Context Scaling for Robot Policiesresearch.nvidia.com
- Ch. 21 - Imitation Learningunderactuated.mit.edu
- e5b5c402bb7bd5e60bede6961d6fe39e-Paper-Conference.pdfproceedings.iclr.cc