Jul 2026· International Journal of Machine Learning and Cybernetics· Vol 17· 0 citations· 55 references
Computer Science
TL;DR
This work proposes RLLMNav, a method that integrates historical experience of RL with commonsense reasoning of LLM through a confidence-gated routing mechanism, and utilizes commonsense knowledge extracted from an LLM to suggest frontiers.
This work proposes a parameter-efficient approach to fine-tune a pretrained VLM for autonomous navigation using an Imperative Learning paradigm, and introduces a unified end-to-end navigation pipeline for natural-language-driven robotic control.
Sebastian Berger, Katharina Winter, Fabian B. Flohr· 0 citations
Embodied navigation requires agents to ground instructions or object goals in spatial observations and translate plans into successful execution. As multimodal large language models (MLLMs) become increasingly capable, they offer stronger support for navigation without task-specific training; however, improved semantic...
Yang Chen, Li-Rong Che, Zhen-Yu Huang et al.· 5 citations· ⚡1
This method formalizes the navigation task as a semiMarkov decision process and constructs a two-layer decision architecture with collaboration between a high-level manager and a low-level worker with collaboration between a high-level manager and a low-level worker.
Qi-Ming Chen· International Conference on...· 0 citations
Vision-Language-Action (VLA) models have emerged as a powerful paradigm for embodied intelligence, but fine-tuning them with reinforcement learning (RL) remains constrained by the cost of real-world robot interaction. Model-based reinforcement learning (MBRL) reduces this cost by using a learned world model to generate...
Yi-Fei Sheng, Hao-Xiang Ren, Zhilong Zhang et al.· 0 citations
The proposed DevGRU navigation system employs an action predictor that generates collision-aware future trajectories, enabling effective avoidance of immediate obstacles and has a relatively small number of trainable parameters, resulting in the fastest inference time among the baselines.
Kyung Min Han, Eunsom Kim, Young J. Kim· IEEE Robotics and Automation...· 0 citations
HAM-VLN is presented, a decision-coupled, agent-authored memory that equips the robot with a persistent, depth-grounded world graph and reduces the context length by more than 65% compared to previous methods.
An Liu, Bingxi Liu, Hongyu Ding et al.· arXiv.org· 1 citation
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