Interactive Navigation in Environments with Traversable Obstacles Using Large Language and Vision-Language Models
CoRR(2023)
摘要
This paper proposes an interactive navigation framework by using large
language and vision-language models, allowing robots to navigate in
environments with traversable obstacles. We utilize the large language model
(GPT-3.5) and the open-set Vision-language Model (Grounding DINO) to create an
action-aware costmap to perform effective path planning without fine-tuning.
With the large models, we can achieve an end-to-end system from textual
instructions like "Can you pass through the curtains to deliver medicines to
me?", to bounding boxes (e.g., curtains) with action-aware attributes. They can
be used to segment LiDAR point clouds into two parts: traversable and
untraversable parts, and then an action-aware costmap is constructed for
generating a feasible path. The pre-trained large models have great
generalization ability and do not require additional annotated data for
training, allowing fast deployment in the interactive navigation tasks. We
choose to use multiple traversable objects such as curtains and grasses for
verification by instructing the robot to traverse them. Besides, traversing
curtains in a medical scenario was tested. All experimental results
demonstrated the proposed framework's effectiveness and adaptability to diverse
environments.
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关键词
traversable obstacles,interactive navigation,vision-language
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