Robotic manipulation in complex open-world scenarios requires both reliable physical manipulation skills and effective and generalizable perception. In this paper, we propose a method where general purpose pretrained visual models serve as an object-centric prior for the perception system of a learned policy. We devise an object-level attentional mechanism that can be used to determine relevant objects from a few trajectories or demonstrations, and then immediately incorporate those objects into a learned policy. A task-independent meta-attention locates possible objects in the scene, and a task-specific attention identifies which objects are predictive of the trajectories. The scope of the task-specific attention is easily adjusted by showing demonstrations with distractor objects or with diverse relevant objects. Our results indicate that this approach exhibits good generalization across object instances using very few samples, and can be used to learn a variety of manipulation tasks using reinforcement learning.
@cite_30: The problem studied in this paper is a mobile robot that autonomously navigates in a domestic environment, builds a map as it moves along and localizes its position in it. In addition, the robot detects predefined objects, estimates their position in the environment and integrates this with the localization module to automatically put the objects in the generated map. Thus, we demonstrate one of the possible strategies for the integration of spatial and semantic knowledge in a service robot scenario where a simultaneous localization and mapping (SLAM) and object detection recognition system work in synergy to provide a richer representation of the environment than it would be possible with either of the methods alone. Most SLAM systems build maps that are only used for localizing the robot. Such maps are typically based on grids or different types of features such as point and lines. The novelty is the augmentation of this process with an object-recognition system that detects objects in the environment and puts them in the map generated by the SLAM system. The metric map is also split into topological entities corresponding to rooms. In this way, the user can command the robot to retrieve a certain object from a certain room. We present the results of map building and an extensive evaluation of the object detection algorithm performed in an indoor setting.
@cite_28: We present an object recognition system which leverages the additional sensing and calibration information available in a robotics setting together with large amounts of training data to build high fidelity object models for a dataset of textured household objects. We then demonstrate how these models can be used for highly accurate detection and pose estimation in an end-to-end robotic perception system incorporating simultaneous segmentation, object classification, and pose fitting. The system can handle occlusions, illumination changes, multiple objects, and multiple instances of the same object. The system placed first in the ICRA 2011 Solutions in Perception instance recognition challenge. We believe the presented paradigm of building rich 3D models at training time and including depth information at test time is a promising direction for practical robotic perception systems.
@cite_34: Robust perception is a vital capability for robotic manipulation in unstructured scenes. In this context, full pose estimation of relevant objects in a scene is a critical step towards the introduction of robots into household environments. In this paper, we present an approach for building metric 3D models of objects using local descriptors from several images. Each model is optimized to fit a set of calibrated training images, thus obtaining the best possible alignment between the 3D model and the real object. Given a new test image, we match the local descriptors to our stored models online, using a novel combination of the RANSAC and Mean Shift algorithms to register multiple instances of each object. A robust initialization step allows for arbitrary rotation, translation and scaling of objects in the test images. The resulting system provides markerless 6-DOF pose estimation for complex objects in cluttered scenes. We provide experimental results demonstrating orientation and translation accuracy, as well a physical implementation of the pose output being used by an autonomous robot to perform grasping in highly cluttered scenes.
@cite_25: This paper proposes a method for detecting known objects in 3D environments and estimating their positions with multiple pan-tilt-zoom cameras. Our search method, Dynamic Active Search, reduces the number of camera operations by predicting the existence of a target in wide angles, zooming-in a promising area, and confirming the target. Even when many reference images taken under various object views and various scales need to be searched for, Dynamic Active Search can detect the object efficiently. With multiple cameras, object detection and object localization become more efficient. Experiments show that Dynamic Active Search with four cameras is 2.5 times faster and 2 times more accurate than with a single camera.
Our method combines prior knowledge about objectness'' from pretrained visual models with an attentional mechanism for learning to detect specific task relevant objects. A number of previous works have sought to combine general objectness priors with more specific object detection in the context of robotics and other visual tasks. used region proposals and SIFT features for quickly detecting objects in the context of SLAM @cite_30 . Prior work used an active search approach where the camera could zoom in certain parts of the receptive field to search at higher resolutions @cite_25 . In manipulation, SIFT features have also been used for 3D pose estimation and object localization, using object-specific training data gathered individually for the task @cite_34 @cite_28 . Similarly to these prior works, our method constrains the observations using an object-centric prior. However, we do not require object level supervision for each task, instead using visual features from a pretrained visual model to index into the proposals from the object-centric prior. This approach drastically reduces the engineering burden for each new task, picking out task-relevant objects from a few demonstrations, and provides good generalization over object appearance, lighting, and scale, as demonstrated in our experiments.