EgoWAM: Bridging the Human-Robot Embodiment Gap via World Representation
danfei_xu · x · 2026-07-09
The research proposes the EgoWAM method to address the performance degradation caused by directly co-training robot policies with in-the-wild human data. Because humans and robots move very differently, EgoWAM bridges this embodiment gap through motion-centric world representations and strong visual pre-training, enabling both to achieve similar results despite their distinct motions.
More from Embodied
- NVIDIA releases MotionBricks: real-time humanoid motion generation at 15,000 FPS with 2ms latency — _akhaliq · 2026-10-26
- Claude Code finds root backdoor in Honda Civic head unit, then debloats it — IndraVahan · 2026-10-26
- Temple health wearable opens pre-orders, users ask: why wear it before benefits are proven — abhishekcode42 · 2026-10-26
- FLASH: GPU-native deformable sim learns cloth folding in minutes on one RTX 5090 — ChongZzZhang · 2026-10-26
- Luke Wroblewski spots it: iPhone Duo's corner radius mirrors the original iPhone — LukeW · 2026-10-26
- Humanoids shattered forecasts: half-marathon record in 2026, 100m faster than Bolt — ben_j_todd · 2026-10-26