Structured Exploration Through Instruction Enhancement for Object Navigation

• URL: http://arxiv.org/abs/2211.08467v1
• Date: Tue, 15 Nov 2022 19:39:22 GMT
• Title: Structured Exploration Through Instruction Enhancement for Object Navigation
• Authors: Matthias Hutsebaut-Buysse, Kevin Mets, Tom De Schepper, Steven Latr\'e
• Abstract summary: We propose a hierarchical learning-based method for object navigation. The top-level is capable of high-level planning, and building a memory on a floorplan-level. We demonstrate the effectiveness of our method on a dynamic domestic environment.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Finding an object of a specific class in an unseen environment remains an unsolved navigation problem. Hence, we propose a hierarchical learning-based method for object navigation. The top-level is capable of high-level planning, and building a memory on a floorplan-level (e.g., which room makes the most sense for the agent to visit next, where has the agent already been?). While the lower-level is tasked with efficiently navigating between rooms and looking for objects in them. Instructions can be provided to the agent using a simple synthetic language. The top-level intelligently enhances the instructions in order to make the overall task more tractable. Language grounding, mapping instructions to visual observations, is performed by utilizing an additional separate supervised trained goal assessment module. We demonstrate the effectiveness of our method on a dynamic configurable domestic environment.

Related papers

• DISCO: Embodied Navigation and Interaction via Differentiable Scene Semantics and Dual-level Control [53.80518003412016]
  Building a general-purpose intelligent home-assistant agent skilled in diverse tasks by human commands is a long-term blueprint of embodied AI research. We study primitive mobile manipulations for embodied agents, i.e. how to navigate and interact based on an instructed verb-noun pair. We propose DISCO, which features non-trivial advancements in contextualized scene modeling and efficient controls.
  arXiv  Detail & Related papers  (2024-07-20T05:39:28Z)
• Embodied Instruction Following in Unknown Environments [66.60163202450954]
  We propose an embodied instruction following (EIF) method for complex tasks in the unknown environment. We build a hierarchical embodied instruction following framework including the high-level task planner and the low-level exploration controller. For the task planner, we generate the feasible step-by-step plans for human goal accomplishment according to the task completion process and the known visual clues.
  arXiv  Detail & Related papers  (2024-06-17T17:55:40Z)
• Aligning Knowledge Graph with Visual Perception for Object-goal Navigation [16.32780793344835]
  We propose the Aligning Knowledge Graph with Visual Perception (AKGVP) method for object-goal navigation. Our approach introduces continuous modeling of the hierarchical scene architecture and leverages visual-language pre-training to align natural language description with visual perception. The integration of a continuous knowledge graph architecture and multimodal feature alignment empowers the navigator with a remarkable zero-shot navigation capability.
  arXiv  Detail & Related papers  (2024-02-29T06:31:18Z)
• Weakly-Supervised Multi-Granularity Map Learning for Vision-and-Language Navigation [87.52136927091712]
  We address a practical yet challenging problem of training robot agents to navigate in an environment following a path described by some language instructions. To achieve accurate and efficient navigation, it is critical to build a map that accurately represents both spatial location and the semantic information of the environment objects. We propose a multi-granularity map, which contains both object fine-grained details (e.g., color, texture) and semantic classes, to represent objects more comprehensively.
  arXiv  Detail & Related papers  (2022-10-14T04:23:27Z)
• PONI: Potential Functions for ObjectGoal Navigation with Interaction-free Learning [125.22462763376993]
  We propose Potential functions for ObjectGoal Navigation with Interaction-free learning (PONI) PONI disentangles the skills of where to look?' for an object and how to navigate to (x, y)?'
  arXiv  Detail & Related papers  (2022-01-25T01:07:32Z)
• Learning Embeddings that Capture Spatial Semantics for Indoor Navigation [2.2940141855172027]
  We study how object embeddings that capture spatial semantic priors can guide search and navigation tasks in a structured environment. We propose a method to incorporate such spatial semantic awareness in robots by leveraging pre-trained language models and multi-relational knowledge bases as object embeddings.
  arXiv  Detail & Related papers  (2021-07-31T06:12:40Z)
• Are We There Yet? Learning to Localize in Embodied Instruction Following [1.7300690315775575]
  Action Learning From Realistic Environments and Directives (ALFRED) is a recently proposed benchmark for this problem. Key challenges for this task include localizing target locations and navigating to them through visual inputs. We augment the agent's field of view during navigation subgoals with multiple viewing angles, and train the agent to predict its relative spatial relation to the target location at each timestep.
  arXiv  Detail & Related papers  (2021-01-09T21:49:41Z)
• ArraMon: A Joint Navigation-Assembly Instruction Interpretation Task in Dynamic Environments [85.81157224163876]
  We combine Vision-and-Language Navigation, assembling of collected objects, and object referring expression comprehension, to create a novel joint navigation-and-assembly task, named ArraMon. During this task, the agent is asked to find and collect different target objects one-by-one by navigating based on natural language instructions in a complex, realistic outdoor environment. We present results for several baseline models (integrated and biased) and metrics (nDTW, CTC, rPOD, and PTC), and the large model-human performance gap demonstrates that our task is challenging and presents a wide scope for future work.
  arXiv  Detail & Related papers  (2020-11-15T23:30:36Z)
• Object Goal Navigation using Goal-Oriented Semantic Exploration [98.14078233526476]
  This work studies the problem of object goal navigation which involves navigating to an instance of the given object category in unseen environments. We propose a modular system called, Goal-Oriented Semantic Exploration' which builds an episodic semantic map and uses it to explore the environment efficiently.
  arXiv  Detail & Related papers  (2020-07-01T17:52:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.