#embodied-ai
4 articles
01
·★ MEMBER·PAPER·10 min read
Paper Walkthrough: GigaBrain-0.7 — 37,000 Hours of Embodied Experience and a Three-System Robot Brain
A ground-up walkthrough of GigaBrain-0.7, which splits a robot foundation model into understanding, prediction/evaluation, and action, then pretrains it on 37,256.98 hours of embodied data in a single stage. Covers subgoal-image and progress-value conditioning, Soft Knowledge Insulation, and the real-robot numbers — strictly from the paper.
02
·Agents·★ MEMBER·PAPER·12 min read
Paper Walkthrough: Embodied-Navigator (TAMP-Nav) — Let the VLM Just Point, and Navigation Gets Both Faster and Better
Instead of asking a VLM for 3D coordinates, have it point at a 2D pixel; think and store memory only at key nodes; align it all with two-level GRPO. A ground-up walkthrough of the design that hits 66.2% SR on R2R-CE at 16.58s per task using only 90k training trajectories.
03
·Agents·★ MEMBER·PAPER·13 min read
Paper walkthrough: Zetta ζ — a robot harness that repairs itself mid-execution, with the policy frozen
Existing embodied agents only reflect once an episode is over. Zetta instead runs code-based critics at action frequency and intervenes the moment a failure signature appears — without touching a single policy weight. A ground-up walkthrough of the design, following the paper itself.
04
·★ MEMBER·PAPER·8 min read
Paper Walkthrough: GST-Bench — Can VLMs Build a Global Map of a Scene from Video?
Show a VLM a walkthrough video of a house, then ask 'from where you're standing now, which way is the sofa?' — even the strongest model scores barely half of what humans do. A walkthrough of GST-Bench from ByteDance Seed: the shortcut-proof benchmark design, results across 22 models, and the training data that closed 27 points of the gap.