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UP-VLA: A Unified Understanding and Prediction Model for Embodied Agent
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Recent advancements in Vision-Language-Action (VLA) models have leveraged pre-trained Vision-Language Models (VLMs) to improve the generalization capabilities. VLMs, typically pre-trained on vision-language understanding tasks, provide rich semantic knowledge and reasoning abilities. However, prior research has shown that VLMs often focus on high-level semantic content and neglect low-level features, limiting their ability to capture detailed spatial information and understand physical dynamics. These aspects, which are crucial for embodied control tasks, remain underexplored in existing pre-training paradigms. In this paper, we investigate the training paradigm for VLAs, and introduce \textbf{UP-VLA}, a \textbf{U}nified VLA model training with both multi-modal \textbf{U}nderstanding and future \textbf{P}rediction objectives, enhancing both high-level semantic comprehension and low-level spatial understanding. Experimental results show that UP-VLA achieves a 33% improvement on the Calvin ABC-D benchmark compared to the previous state-of-the-art method. Additionally, UP-VLA demonstrates improved success rates in real-world manipulation tasks, particularly those requiring precise spatial information.
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Cited by 7 Pith papers
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VLM4VLA: Revisiting Vision-Language-Models in Vision-Language-Action Models
Using a simple action-token adapter, nine VLMs are compared as robot policy backbones, showing general VLM ability transfers poorly to control and the vision encoder is the key bottleneck.
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UniSteer: Unified Noise Steering for Efficient Human-Guided VLA Adaptation
UniSteer unifies human corrective actions and noise-space RL for VLA adaptation by inverting actions to noise targets, raising success rates from 20% to 90% in 66 minutes across four real-world manipulation tasks.
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Source Component Shift Adaptation via Offline Decomposition and Online Mixing Approach
The claimed result is that source-component-shift adaptation splits cleanly into offline component learning via EM and online mixing-weight updates, cutting cumulative test loss by up to 67.4%.
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Improving Generalization of Language-Conditioned Robot Manipulation
A two-stage fine-tuning framework with instance-level semantic fusion lets language-conditioned robots learn object-arrangement tasks from a few demonstrations and generalize to unseen environments.
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ReFineVLA: Reasoning-Aware Teacher-Guided Transfer Fine-Tuning
Fine-tuning a vision-language-action robot model on teacher-generated reasoning rationales raises average simulated manipulation success by up to 8.6 percentage points over the SpatialVLA baseline.
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StemVLA:An Open-Source Vision-Language-Action Model with Future 3D Spatial Geometry Knowledge and 4D Historical Representation
StemVLA supervises a GPT-2-based VLA with predicted future 3D-geometry features (VGGT) and temporally aggregated history, reporting 86.0% on LIBERO-Long - but its CALVIN results and equations are placeholders.
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Leveraging OS-Level Primitives for Robotic Action Management
Applying OS-style exception handling, context caching, and replay to robotic action slices raises success rates 7x to 24x and cuts execution steps up to 74% for repetitive manipulation tasks, without retraining the VLA model.
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