A novel training-free Condensed Dynamic Adapter C ON DA is proposed, which leverages vision-text alignment to guide vision-vision alignment and is compatible with arbitrary VLM and generalizes well across complex scenarios, such as long-term and egocentric scenarios.
Vision-Language-Action (VLA) policies are commonly adapted to new manipulation settings through additional gradient updates, which limits rapid deployment when task-specific data or compute is scarce. We present ICI-VLA, a training and retrieval framework that equips a text-action VLM with few-shot test-time adaptation through in-context demonstrations. Unlike mainstream VLA designs based on action-specific multimodal fusion, ICI-VLA retains the native text-generation interface. ICI-VLA updates its parameters only during offline training; at inference, the policy remains fixed and conditions action generation on retrieved micro-demonstrations. The framework decomposes long trajectories into short, semantically labeled examples and trains an RD-Encoder with positives mined by Dynamic Time Warping (DTW), aligning the retrieved context with the phase and geometry of the current subtask. We further introduce Target Action Masking, a context-corruption objective designed to reduce direct action copying and increase reliance on the current observation. ICI-VLA reaches average success rates of 97.7% on LIBERO and 60.4% on RoboTwin 2.0, exceeding the highest reported baseline average on RoboTwin 2.0 by 19.3 percentage points. It also achieves 83.2% across four physical tasks. These results indicate that a fixed VLA policy can benefit from conditioning on spatiotemporally aligned demonstrations at test time.
UniMem is presented, a framework that unifies high-level, multimodal memory and low-level control under one backbone that outperforms fixed-interval image sampling baselines in simulation and hierarchical baselines in hardware, while offering faster inference and a simple training pipeline for easy adoption.
Lars W. Osterberg, M. Wang, Mac Schwager· 0 citations
A novel metric derived from the flow matching trajectory curvature is introduced to quantify action generation confidence during inference and enables the dynamic reduction of inference steps and the adaptive adjustment of MLP pruning ratios through an efficiently computed importance evaluation, requiring no access to training data.
A reliable TTT framework for VLA policies (VANE), where candidate updates are isolated from the live policy, evaluated on subsequent observations, and committed only when supported by future evidence, making adaptation selective and reversible.
H. Ji, Guo-Yang Xia, Luo-Yang Sun et al.· 0 citations
Mechanistic analyses show that HAFI restores task-dependent spectral allocation while retaining semantic attention, establishing frequency enrichment as a distinct and effective route for improving VLM perception.
Jin Cui, Chuanchang Su, Jia-Yi Lu et al.· 0 citations
This work introduces ActionLMM, a memory-augmented vision-language model for long-video action summarization that aligns visual and motion modalities through joint representation learning and leverages a novel dual-memory mechanism to retain both local motion details and global temporal structure.
Rui-Rui Li, Dari Abdullah Alrwoaily, Turgut Sofuyev et al.· International Conference on...· 0 citations
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