Benchmarking memory-augmented robotic generalist policies • 18 items • Updated • 3
MME-VLA Checkpoints
Arxiv Paper | HF Paper | Website | Benchmark Code | Policy Learning Code
This repository contains fine-tuned checkpoints for MME-VLA-Suite.
We train 14 VLA variants in total, all based on the $\pi_{0.5}$ model.
| Name | Memory type | Memory representation | Memory integration | AVG Success | Released |
|---|---|---|---|---|---|
| symbolic-simple-subgoal | symbolic | simple subgoal | language concatenation | 29.00 | ✅ |
| symbolic-grounded-subgoal | symbolic | grounded subgoal | language concatenation | 33.06 | ✅ |
| perceptual-tokendrop-context | perceptual | token dropping | memory-as-context | 34.50 | ✅ |
| perceptual-tokendrop-modul | perceptual | token dropping | memory-as-modulation | 38.04 | ✅ |
| perceptual-tokendrop-expert | perceptual | token dropping | memory-as-expert | 34.86 | ✅ |
| perceptual-framesamp-context | perceptual | frame sampling | memory-as-context | 30.68 | ✅ |
| perceptual-framesamp-modul | perceptual | frame sampling | memory-as-modulation | 44.51 | ✅ |
| perceptual-framesamp-expert | perceptual | frame sampling | memory-as-expert | 36.25 | ✅ |
| recurrent-ttt-context | recurrent | TTT | memory-as-context | 22.28 | ✅ |
| recurrent-ttt-modul | recurrent | TTT | memory-as-modulation | 21.97 | ❌ |
| recurrent-ttt-expert | recurrent | TTT | memory-as-expert | 22.35 | ✅ |
| recurrent-rmt-context | recurrent | RMT | memory-as-context | 19.46 | ❌ |
| recurrent-rmt-modul | recurrent | RMT | memory-as-modulation | 20.17 | ❌ |
| recurrent-rmt-expert | recurrent | RMT | memory-as-expert | 18.15 | ❌ |
We release all symbolic and perceptual memory MME-VLA variants for research.
Due to recurrent memory currently underperforms, we release only a subset. We will release newer recurrent variants once we obtain better results.
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