HKU's TILDE Distills Embodied Foundation Models into Robot Policies
hkuhk · hf · 2026-10-23
HKU researchers propose TILDE, distilling embodied foundation model capabilities into robot policies via on-policy teacher supervision and autonomous recovery data collection, improving real-world performance without large-scale action pretraining. Figures show gains on LIBERO benchmarks over m0.5, X-VLA and VLA-Adapter-Pro baselines.
More from Embodied
- 10 of 12 Beta Robots Worked Without Fine-Tuning — Do We Still Need It? — micoolcho · 2026-10-23
- EmbodiedEvo lets robot agents self-train, banking reusable Skills and Lessons across tasks — jiqizhixin · 2026-10-23
- KAIST's FRED lifts robot policy success from 19.4% to 50% via self-distilled future video representations — kaist-ai · 2026-10-23
- GALAX Launches World's First RTX 5080 With Dual 12V-2x6 Connectors at $3000 — chemist_slime · 2026-10-23
- Figure prints 81 minutes of robot data per second, spent ~$8.1M on data last week — joecole · 2026-10-23
- Just three physical AI agents are actually paid for at scale, argues investor — jokrvivek · 2026-10-23