LLM Digest

Story

arxiv_cs_lg · Sep 15, 2026 · paper

Source brief

TEMPO: Learning Temporal Context for Dynamic Robot Manipulation

arxiv.orgSep 15, 2026
original source linked

In brief

Vision-language-action (VLA) models have achieved impressive performance in quasi-static manipulation, but struggle in dynamic manipulation tasks because they operate on a single observation at inference time. We iden...

Feed lens

eval

Read the original at arxiv.org →Open in live feed Read that day’s brief

TEMPO: Learning Temporal Context for Dynamic Robot Manipulation

Earlier in this thread 4 items

Robot Learning from Human Demonstrations: Handwritten Alphabet Trajectories and Human-Likeness Evaluation

Bench2Dex: Benchmarking Visuo-Tactile Bimanual Dexterous Manipulation Across Dexterous Hands

Behavior Quotient Learning for Low-Rank Adaptation of LLM Agents

RATL: Learning from Retrieved Residuals for Robust Multivariate Time-Series Forecasting