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
This work introduces HarnessVLN, a zero-shot, training-free framework that unifies instruction-following and object-goal navigation through a shared Agent Harness and demonstrates its applicability to both navigation tasks in real-world environments.
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, Zhi-Long 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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