UP-VLA: A Unified Understanding and Prediction Model for Embodied Agent (2501.18867v3)

Abstract: Recent advancements in Vision-Language-Action (VLA) models have leveraged pre-trained Vision-LLMs (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.

• The paper introduces UP-VLA, a unified Vision-Language-Action model addressing VLM limitations by integrating high-level understanding and low-level spatial prediction for embodied agents.
• UP-VLA demonstrates significant improvements, including a 33% gain over previous methods on the Calvin ABC-D benchmark and enhanced real-world manipulation success rates.
• This work highlights the potential of co-training future prediction with multi-modal understanding to improve semantic generalization and spatial understanding in robotic policies.

The paper under discussion, titled "UP-VLA: A Unified Understanding and Prediction Model for Embodied Agent," describes the development and application of a Unified Vision-Language-Action (UP-VLA) model that advances the integration of multi-modal understanding and spatial prediction for embodied agents. This work addresses the limitations present in traditional Vision-LLMs (VLMs) which often overlook low-level features and spatial details necessary for effective embodied control tasks. The authors aim to coalesce high-level semantic understanding with precise low-level spatial comprehension, thereby enhancing embodied decision-making in open environments.

Key Contributions and Methodology

1. Motivation and Approach: The paper identifies the shortcomings of existing VLMs in capturing detailed spatial features and physical dynamics, which are critical for robotic applications. Existing pre-training paradigms focus heavily on high-level tasks, like Visual Question Answering (VQA), which deprioritize spatial and dynamic understanding. The authors introduce a novel training regime that incorporates both high-level comprehension and low-level prediction tasks.
2. Unified Training Paradigm: UP-VLA employs an autoregressive modeling scheme augmented by an attention mechanism capable of bridging the gap between vision-language understanding tasks and predictive modeling tasks. The framework simultaneously handles three types of datasets, encompassing multi-modal understanding, future prediction, and action generation to enhance embodied decision-making.
3. Numerical Results and Evaluation: The UP-VLA model demonstrates a 33% improvement over previous methods on the Calvin ABC-D benchmark, showcasing superior multitask learning and adaptability in simulated and real-world environments. Experimental evidence further suggests enhanced performance in tasks demanding precise spatial reasoning, with UP-VLA notably improving upon real-world manipulation success rates.

Experimental Framework

• Simulated and Real-World Evaluations: The proposed model exhibits expansive capabilities across both simulated environments and real-world table-top manipulation tasks. In simulations, the model exhibits superior task completion rates on long-horizon language-conditioned benchmarks (Calvin ABC-D), substantiating its capability in handling unseen tasks and precise operations.
• Architectural Details: UP-VLA employs the Phi-1.5 LLM as a backbone, utilizing a CLIP-ViT encoder to project images into a language embedding space. VQ-GAN encoders are used for discrete image tokenization, aiding future prediction tasks by predicting future image tokens rather than denoising processes.

Conclusions

The authors showcase a pioneering framework capable of integrating visual prediction capabilities into the VLM paradigm. UP-VLA's co-training of future prediction with multi-modal understanding objectives substantiates significant effectiveness in semantic generalization and spatial understanding. The approach highlights the potential to elevate current VLA methodologies by robustly embedding thorough semantic grounding with exhaustive spatial comprehension, hence, facilitating better decision-making prowess in robotic policies.

The paper asserts that integrating predictions of future states and sophisticated understanding of real-world spatial dynamics can be pivotal for advancing the field of embodied AI, ensuring consistency in both visual generalization and task precision. The insights offered pave a promising path towards holistic robotic systems that can adeptly navigate complex physical environments while understanding nuanced multi-modal cues.