SmoLSTM: エピソード記憶を持続するコンパクトな視覚-言語-行動モデル
SmoLSTM: A Compact Vision-Language-Action Model with Recurrent Memory that Persists
凍結したSmolVLMとLSTM制御層を組み合わせ、エピソード全体でリセットしない再帰状態により、遮蔽や見分けがつかない物体を含むタスクを少ないパラメータで高精度に実行するVLAモデルを提案。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Jan-Gerrit Habekost, Parsa Mastouri Kashani, Connor Gäde, Matthias Kerzel, Philipp Allgeuer, Cornelius Weber, Stefan Wermter, Jae Hee Lee
分類: cs.RO, cs.CV
原文アブストラクト
Vision-language-action models often predict actions from only the current observation, which can leave tasks involving object occlusion or visually identical objects ambiguous without episode history. The usual countermeasure, widening the observation window, turns the horizon into a hyperparameter and lets per-step cost grow with it. We instead capture the episode in a recurrent state. SmoLSTM couples a frozen 256M-parameter SmolVLM backbone to a matrix-memory LSTM control layer in which observation tokens and action queries are unified in a single causal stream that is never reset throughout the entire episode. Recurrent-state storage is therefore O(1) in episode length. A flow-matching action head predicts chunks of 10 end-effector pose deltas and gripper commands at each control step. Our single policy, trained jointly on 7,461 demonstrations across 140 tasks and evaluated on held-out initial states, performs best, reaching 85.1% subgoal coverage and 77.5% full-task success on LIBERO-Mem with 0.04B trainable parameters, surpassing both the benchmark's own object-centric baseline and recent memory-based approaches. Resetting the recurrent state at every control step reduces full-task success to 7.0%, showing that the trained policy relies on context carried between decisions. The same model achieves 79.6% average success on standard LIBERO.
