Vision-and-Language Navigation in Continuous Environments (VLN-CE) requires agents to execute sequential navigation actions in complex environments guided by natural language instructions. Current approaches often struggle with generalizing to novel environments and adapting to ongoing changes during navigation. Inspired by human cognition, we present NavMorph, a self-evolving world model framework that enhances environmental understanding and decision-making in VLN-CE tasks. NavMorph employs compact latent representations to model environmental dynamics, equipping agents with foresight for adaptive planning and policy refinement. By integrating a novel Contextual Evolution Memory, NavMorph leverages scene-contextual information to support effective navigation while maintaining online adaptability. Extensive experiments demonstrate that our method achieves notable performance improvements on popular VLN-CE benchmarks. Code is available at https://github.com/Feliciaxyao/NavMorph.

Embodied artificial intelligence has rapidly developed under the impetus of multimodal learning, robotics, and cognitive science, demonstrating great potential in fields such as navigation and manipulation. However, building embodied agents that can robustly operate in diverse and dynamic environments still faces challenges, such as handling partial observability and environmental adaptability. Multimodal large language models (MLLMs) are vital for embodied intelligence due to their ability to process multimodal information, but they encounter difficulties in understanding spatial environments and performing dynamic decisions and evolution. Inspired by the functional specialization of the left and right hemispheres of the human brain, this paper proposes a brain-inspired learning and evolution paradigm for embodied agents. The method designs an embodied context-augmented MLLM to simulate the language processing and logical analysis capabilities of the left hemisphere, responsible for understanding instructions and visual scenes. At the same time, it constructs a perceptual context-guided world model based on the recurrent state space model to simulate the spatial perception and holistic thinking functions of the right hemisphere, capturing environmental dynamics and predicting future states. By simulating the communication function of the corpus callosum, we propose dynamic communication slots for efficient information exchange between MLLMs and the world model, which also allows the agent to quickly adapt to dynamic environments without requiring extensive computational resources. Experiments show that the proposed paradigm significantly improves the performance of embodied agents in a series of tasks and enhances their generalization ability in zero-shot tasks through embodied exploration experience and online evolution. Our project page is available at https://feliciaxyao.github.io/EvoAgent/.

Most cross-modal methods assume that training and testing data come from the same domain, which is often not the case in real-world scenarios due to cross-modal domain shifts and potential unknown concepts. Moreover, cross-modal shifts hinder the capture of unknown concepts, and the presence of unknown concepts can in turn exacerbate the cross-modal shifts. To address these challenges, this paper proposes a new paradigm called Active Cross-Modal Domain Adaptation (ACM-DA), wherein only cross-modal data from the source domain and uni-modal data from the target domain are utilized. To concurrently mitigate the adverse effects of both cross-modal domain shifts and unknown concepts, we propose a Curiosity-Driven Active Adaptation Network (CD-A2N), selectively annotating samples to maximize performance gain. First, we present Curiosity Arousal within Cross-modal Domain Adaptation (CA-CDA) to explore the complexity and novelty characteristics of target samples, while reducing cross-modal discrepancy and aligning source and target domains. Second, Curiosity-driven Active Learning (CAL) is devised to strategically select a subset of target samples for annotation, aiming to achieve more valuable data selection at a small labeling cost. Finally, we jointly train CA-CDA and CAL with the newly labeled target domain sub-dataset to alleviate the above issues. Extensive experiments demonstrate that CD-A2N provides an effective solution for achieving ACM-DA. Code will be available athttps://github.com/Feliciaxyao/ACM-DA.

The ability to accurately comprehend natural language instructions and navigate to the target location is essential for an embodied agent. Such agents are typically required to execute user instructions in an online manner, leading us to explore the use of unlabeled test samples for effective online model adaptation. However, for online Vision-and-Language Navigation (VLN), due to the intrinsic nature of inter-sample online instruction execution and intra-sample multi-step action decision, frequent updates can result in drastic changes in model parameters, while occasional updates can make the model ill-equipped to handle dynamically changing environments. Therefore, we propose a Fast-Slow Test-Time Adaptation (FSTTA) approach for online VLN by performing joint decomposition-accumulation analysis for both gradients and parameters in a unified framework. Extensive experiments show that our method obtains impressive performance gains on four popular benchmarks. Code is available at https://github.com/Feliciaxyao/ICML2024-FSTTA.

This paper targets at the task of video entailment, which aims to achieve a thorough comprehension and draw inferences on whether a natural language statement entails or contradicts a given multi-modal video. Despite the recent progress, most existing methods focus on designing a vision-language encoder for multi-modal feature extraction in video entailment, which ignore the underlying consensus knowledge between two modalities, hindering the reasoning performance. As human beings, we make sense of the world by synthesizing information from different sense perceptions, which can acquire consensus among multiple modalities to form a more thorough and coherent representation of the surroundings, as well as to perform complicated understanding tasks. In this paper, we attempt to recreate this ability to infer the truthfulness of a given statement in the context of video entailment. To this end, we propose a unified structure-aware cross-modal consensus method to excavate the consensus semantics shared between video and language modalities, thereby incorporating which into video entailment as statement-related clues. Specifically, the consensus information is achieved by filtering away redundant information by utilizing the global information from one modality and the local complementary information from the other one. Moreover, a consensus-guided graph reasoning method is designed to explore inter-modality consistency and emphasize the significant features related to the judged statement, generating the inference results. Extensive experiments on two benchmarks demonstrate the accurate and robust performance of our approach compared to state-of-the-arts. Code is available at https://github.com/Feliciaxyao/MM2023-SACCN.