Fit to Measure: Reasoning about Sizes for Robust Object Recognition

• URL: http://arxiv.org/abs/2010.14296v1
• Date: Tue, 27 Oct 2020 13:54:37 GMT
• Title: Fit to Measure: Reasoning about Sizes for Robust Object Recognition
• Authors: Agnese Chiatti, Enrico Motta, Enrico Daga, Gianluca Bardaro
• Abstract summary: We present an approach to integrating knowledge about object sizes in a ML based architecture. Our experiments in a real world robotic scenario show that this combined approach ensures a significant performance increase over state of the art Machine Learning methods.
• Score: 0.5352699766206808
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Service robots can help with many of our daily tasks, especially in those cases where it is inconvenient or unsafe for us to intervene: e.g., under extreme weather conditions or when social distance needs to be maintained. However, before we can successfully delegate complex tasks to robots, we need to enhance their ability to make sense of dynamic, real world environments. In this context, the first prerequisite to improving the Visual Intelligence of a robot is building robust and reliable object recognition systems. While object recognition solutions are traditionally based on Machine Learning methods, augmenting them with knowledge based reasoners has been shown to improve their performance. In particular, based on our prior work on identifying the epistemic requirements of Visual Intelligence, we hypothesise that knowledge of the typical size of objects could significantly improve the accuracy of an object recognition system. To verify this hypothesis, in this paper we present an approach to integrating knowledge about object sizes in a ML based architecture. Our experiments in a real world robotic scenario show that this combined approach ensures a significant performance increase over state of the art Machine Learning methods.

Related papers

• Learning Object Properties Using Robot Proprioception via Differentiable Robot-Object Interaction [52.12746368727368]
  Differentiable simulation has become a powerful tool for system identification. Our approach calibrates object properties by using information from the robot, without relying on data from the object itself. We demonstrate the effectiveness of our method on a low-cost robotic platform.
  arXiv  Detail & Related papers  (2024-10-04T20:48:38Z)
• Localizing Active Objects from Egocentric Vision with Symbolic World Knowledge [62.981429762309226]
  The ability to actively ground task instructions from an egocentric view is crucial for AI agents to accomplish tasks or assist humans virtually. We propose to improve phrase grounding models' ability on localizing the active objects by: learning the role of objects undergoing change and extracting them accurately from the instructions. We evaluate our framework on Ego4D and Epic-Kitchens datasets.
  arXiv  Detail & Related papers  (2023-10-23T16:14:05Z)
• Challenges for Monocular 6D Object Pose Estimation in Robotics [12.037567673872662]
  We provide a unified view on recent publications from both robotics and computer vision. We find that occlusion handling, novel pose representations, and formalizing and improving category-level pose estimation are still fundamental challenges. In order to address them, ontological reasoning, deformability handling, scene-level reasoning, realistic datasets, and the ecological footprint of algorithms need to be improved.
  arXiv  Detail & Related papers  (2023-07-22T21:36:57Z)
• Lifelong Ensemble Learning based on Multiple Representations for Few-Shot Object Recognition [6.282068591820947]
  We present a lifelong ensemble learning approach based on multiple representations to address the few-shot object recognition problem. To facilitate lifelong learning, each approach is equipped with a memory unit for storing and retrieving object information instantly. We have performed extensive sets of experiments to assess the performance of the proposed approach in offline, and open-ended scenarios.
  arXiv  Detail & Related papers  (2022-05-04T10:29:10Z)
• From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence [113.06484656032978]
  Article argues that embodied intelligence is a key driver for the advancement of machine learning technology. We highlight challenges and opportunities specific to embodied intelligence. We propose research directions which may significantly advance the state-of-the-art in robot learning.
  arXiv  Detail & Related papers  (2021-10-28T16:04:01Z)
• Maintaining a Reliable World Model using Action-aware Perceptual Anchoring [4.971403153199917]
  There is a need for robots to maintain a model of its surroundings even when objects go out of view and are no longer visible. This requires anchoring perceptual information onto symbols that represent the objects in the environment. We present a model for action-aware perceptual anchoring that enables robots to track objects in a persistent manner.
  arXiv  Detail & Related papers  (2021-07-07T06:35:14Z)
• Simultaneous Multi-View Object Recognition and Grasping in Open-Ended Domains [0.0]
  We propose a deep learning architecture with augmented memory capacities to handle open-ended object recognition and grasping simultaneously. We demonstrate the ability of our approach to grasp never-seen-before objects and to rapidly learn new object categories using very few examples on-site in both simulation and real-world settings.
  arXiv  Detail & Related papers  (2021-06-03T14:12:11Z)
• Attribute-Based Robotic Grasping with One-Grasp Adaptation [9.255994599301712]
  We introduce an end-to-end learning method of attribute-based robotic grasping with one-grasp adaptation capability. Our approach fuses the embeddings of a workspace image and a query text using a gated-attention mechanism and learns to predict instance grasping affordances. Experimental results in both simulation and the real world demonstrate that our approach achieves over 80% instance grasping success rate on unknown objects.
  arXiv  Detail & Related papers  (2021-04-06T03:40:46Z)
• Cognitive architecture aided by working-memory for self-supervised multi-modal humans recognition [54.749127627191655]
  The ability to recognize human partners is an important social skill to build personalized and long-term human-robot interactions. Deep learning networks have achieved state-of-the-art results and demonstrated to be suitable tools to address such a task. One solution is to make robots learn from their first-hand sensory data with self-supervision.
  arXiv  Detail & Related papers  (2021-03-16T13:50:24Z)
• Unadversarial Examples: Designing Objects for Robust Vision [100.4627585672469]
  We develop a framework that exploits the sensitivity of modern machine learning algorithms to input perturbations in order to design "robust objects" We demonstrate the efficacy of the framework on a wide variety of vision-based tasks ranging from standard benchmarks to (in-simulation) robotics.
  arXiv  Detail & Related papers  (2020-12-22T18:26:07Z)
• A Review on Intelligent Object Perception Methods Combining Knowledge-based Reasoning and Machine Learning [60.335974351919816]
  Object perception is a fundamental sub-field of Computer Vision. Recent works seek ways to integrate knowledge engineering in order to expand the level of intelligence of the visual interpretation of objects.
  arXiv  Detail & Related papers  (2019-12-26T13:26:49Z)

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.