GN (Pangu Navigator), an offline VLN action-prediction system built on OpenPangu-7B, combines mixed-precision computation, selective FP32 computation, and DeepSpeed ZeRO-2 on eight Ascend 910B NPUs and reports metrics quantify offline expert-action alignment rather than closed-loop navigation success.
Abstract
Vision-Language Navigation (VLN) requires an embodied agent to interpret a natural-language instruction and predict actions from temporally ordered visual observations. Adapting a multimodal large language model to VLN requires visual-language alignment, compact temporal inputs, action-space grounding, and stable training on the target hardware. This technical report presents PGN (Pangu Navigator), an offline VLN action-prediction system built on OpenPangu-7B. Training proceeds in two stages. First, PGMM aligns a frozen EVA-ViT-G/14 vision encoder with the frozen language backbone by training a Q-Former and a two-layer MLP projector. Second, PGN adapts the aligned model to expert navigation trajectories using five-observation windows, epoch-dependent temporal sampling, and a reasoning-then-action output format; this stage freezes the aligned visual pathway and updates three structural-token embeddings and LoRA adapters. The implementation combines mixed-precision computation, selective FP32 computation, and DeepSpeed ZeRO-2 on eight Ascend 910B NPUs. Under teacher-forced, open-loop evaluation on 500 held-out expert trajectories, V9 reports a 62.29% Normalized Action Match (NAM) and a 100.00% Non-empty Rate (NER). These metrics quantify offline expert-action alignment rather than closed-loop navigation success; evaluating error accumulation, path efficiency, and goal completion remains future work.
Vision-Language Navigation (VLN) requires an embodied agent to follow natural-language instructions in unseen environments. Recent progress has been largely driven by Multimodal Large Language Models (MLLMs). Existing methods follow a next-step action prediction paradigm, supervising only the expert action, which requires a high quantity of data for training. They also rely on cognitive maps, accumulated historical frames, or external 3D tools to maintain states, leading to high computational and memory overhead. To realize resource efficiency VLN, we propose LookStep, a unified end-to-end framework that combines Language Centric Future State Modeling and Event Driven Rolling Memory that uses language labels to generate coarse-grained navigation progress and future states for each candidate action, while autonomously deciding whether to write each observation into a bounded rolling memory with a semantic role. We validate LookStep empirically. On VLN-CE tasks, LookStep outperforms existing methods under the same training settings, achieving a 49.7\% success rate on R2R-CE Val-Unseen with better memory efficiency and less data usage. Code and model is available at https://github.com/kunyang-YU/LookStep.
Kun-Yang Yu, Ying-Zhe Li, Hongyu Xu et al.· 0 citations
The real-robot benchmark demonstrates that StellaVLA can use both human/robot demos and human-to-robot (XR) demos as in-context structured demonstration to help VLA model adapt to OOD tasks.
Siyu Xu, Yunke Wang, Zijian Wang et al.· 0 citations
MemVLN is proposed, a novel VLN framework that achieves state-of-the-art performance with real-time inference efficiency (14 FPS) and introduces Procedural Memory for fast action with a compact vocabulary of atomic mid-level actions to bypass auto-regressive decoding latency.
Vision-and-Language Navigation (VLN) is a representative task in embodied artificial intelligence, requiring agents to perceive, understand, and make navigation decisions in partially observable environments according to natural language instructions. As research has expanded from early discrete simulation benchmarks to continuous control, interactive clarification, open-vocabulary perception, and real-world robotic deployment, VLN has evolved from a path-following multimodal task into an important research area connecting language understanding, environment modeling, spatial reasoning, and embodied execution. Existing surveys mainly organize the literature by timeline, model paradigm, or benchmark, while paying less attention to the internal components of VLN systems and their functional coupling. In this survey, we revisit VLN from a component-internal perspective, viewing it as a navigation system composed of internal components such as instructions, environment representations, and embodied agents, and organizing existing work around the functions and interactions of these components. Specifically, we summarize task definitions, datasets, and evaluation settings, and review representative methods and technical progress in instruction understanding and action generation, instruction–environment alignment, and robot–environment interaction understanding. We further discuss key trends as VLN moves from closed benchmarks toward open-world and real-world deployment, including reasoning-enhanced planning, open-vocabulary and online semantic mapping, long-horizon memory and structured spatial representation, and sim-to-real transfer across platforms. We hope this survey provides a clearer component-level analytical framework for understanding the evolution of internal VLN capabilities and for informing future method design and embodied-system deployment.
The proposed hierarchical long-horizon VLA architecture with an explicit language-memory module improves the success rate and robustness of VLA models on complex long-horizon tasks while providing an interpretable semantic account of the decision process.
ALVA (Action- and Language-Conditioned Video Assessment), a trajectory evaluator that conditions its assessment on visual observations, the executed action sequence, and the natural language instruction, provides more effective feedback than the evaluated static image and embedding-based visual baselines and reduces the performance gap to a ground-truth oracle.
Hwanhee Kim, Jaehyun Jang, Seung-Min Cha et al.· Italian National Conference...· 0 citations
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