ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objects

• URL: http://arxiv.org/abs/2006.13171v2
• Date: Sun, 30 Aug 2020 04:28:13 GMT
• Title: ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objects
• Authors: Dhruv Batra, Aaron Gokaslan, Aniruddha Kembhavi, Oleksandr Maksymets, Roozbeh Mottaghi, Manolis Savva, Alexander Toshev, Erik Wijmans
• Abstract summary: This document summarizes the consensus recommendations of a working group on ObjectNav. We make recommendations on subtle but important details of evaluation criteria. We provide a detailed description of the instantiation of these recommendations in challenges organized at the Embodied AI workshop at CVPR 2020.
• Score: 119.46959413000594
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We revisit the problem of Object-Goal Navigation (ObjectNav). In its simplest form, ObjectNav is defined as the task of navigating to an object, specified by its label, in an unexplored environment. In particular, the agent is initialized at a random location and pose in an environment and asked to find an instance of an object category, e.g., find a chair, by navigating to it. As the community begins to show increased interest in semantic goal specification for navigation tasks, a number of different often-inconsistent interpretations of this task are emerging. This document summarizes the consensus recommendations of this working group on ObjectNav. In particular, we make recommendations on subtle but important details of evaluation criteria (for measuring success when navigating towards a target object), the agent's embodiment parameters, and the characteristics of the environments within which the task is carried out. Finally, we provide a detailed description of the instantiation of these recommendations in challenges organized at the Embodied AI workshop at CVPR 2020 http://embodied-ai.org .

Related papers

• Personalized Instance-based Navigation Toward User-Specific Objects in Realistic Environments [44.6372390798904]
  We propose a new task denominated Personalized Instance-based Navigation (PIN), in which an embodied agent is tasked with locating and reaching a specific personal object. In each episode, the target object is presented to the agent using two modalities: a set of visual reference images on a neutral background and manually annotated textual descriptions.
  arXiv  Detail & Related papers  (2024-10-23T18:01:09Z)
• Prioritized Semantic Learning for Zero-shot Instance Navigation [2.537056548731396]
  We study zero-shot instance navigation, in which the agent navigates to a specific object without using object annotations for training. We propose a Prioritized Semantic Learning (PSL) method to improve the semantic understanding ability of navigation agents. Our PSL agent outperforms the previous state-of-the-art by 66% on zero-shot ObjectNav in terms of success rate and is also superior on the new InstanceNav task.
  arXiv  Detail & Related papers  (2024-03-18T10:45:50Z)
• Right Place, Right Time! Towards ObjectNav for Non-Stationary Goals [55.581423861790945]
  We present a novel approach to tackle the ObjectNav task for non-stationary and potentially occluded targets in an indoor environment. We present its formulation, feasibility, and a navigation benchmark using a novel memory-enhanced LLM-based policy.
  arXiv  Detail & Related papers  (2024-03-14T22:33:22Z)
• Find What You Want: Learning Demand-conditioned Object Attribute Space for Demand-driven Navigation [5.106884746419666]
  The task of Visual Object Navigation (VON) involves an agent's ability to locate a particular object within a given scene. In real-world scenarios, it is often challenging to ensure that these conditions are always met. We propose Demand-driven Navigation (DDN), which leverages the user's demand as the task instruction.
  arXiv  Detail & Related papers  (2023-09-15T04:07:57Z)
• Instance-Specific Image Goal Navigation: Training Embodied Agents to Find Object Instances [90.61897965658183]
  We consider the problem of embodied visual navigation given an image-goal (ImageNav) Unlike related navigation tasks, ImageNav does not have a standardized task definition which makes comparison across methods difficult. We present the Instance-specific ImageNav task (ImageNav) to address these limitations.
  arXiv  Detail & Related papers  (2022-11-29T02:29:35Z)
• SOON: Scenario Oriented Object Navigation with Graph-based Exploration [102.74649829684617]
  The ability to navigate like a human towards a language-guided target from anywhere in a 3D embodied environment is one of the 'holy grail' goals of intelligent robots. Most visual navigation benchmarks focus on navigating toward a target from a fixed starting point, guided by an elaborate set of instructions that depicts step-by-step. This approach deviates from real-world problems in which human-only describes what the object and its surrounding look like and asks the robot to start navigation from anywhere.
  arXiv  Detail & Related papers  (2021-03-31T15:01:04Z)
• 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)

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.