This paper explores the problem of task learning and planning, contributing the Action-Category Representation (ACR) to improve computational performance of both Planning and Reinforcement Learning (RL). ACR is an algorithm-agnostic, abstract data representation that maps objects to action categories (groups of actions), inspired by the psychological concept of action codes. We validate our approach in StarCraft and Lightworld domains; our results demonstrate several benefits of ACR relating to improved computational performance of planning and RL, by reducing the action space for the agent.
in @cite_23 learn visual object categories for affordance prediction (Category-Affordance model), reducing the physical interactions with the objects. They use visual features of objects to categorize them on the basis of their functionality. However, it is unclear how the agent would deal with changing features and categories @cite_0 , since the model is learned offline as compared to ACR which allows online learning of new objects and categories (Details in Sec 3). Regardless, their approach highlights some of the benefits of categorization on the scalability of learning, which motivates our work.
@cite_0 @cite_23
2091938290 1989739075 1891689858 1989695841
A fundamental requirement of any autonomous robot system is the ability to predict the affordances of its environment. The set of affordances define the actions that are available to the agent given the robotâs context. A standard approach to affordance learning is direct perception, which learns direct mappings from sensor measurements to affordance labels. For example, a robot designed for cross-country navigation could map stereo depth information and image features directly into predictions about the traversability of terrain regions. While this approach can succeed for a small number of affordances, it does not scale well as the number of affordances increases. In this paper, we show that visual object categories can be used as an intermediate representation that makes the affordance learning problem scalable. We develop a probabilistic graphical model which we call the CategoryâAffordance (CA) model, which describes the relationships between object categories, affordances, and appearance. This model casts visual object categorization as an intermediate inference step in affordance prediction. We describe several novel affordance learning and training strategies that are supported by our new model. Experimental results with indoor mobile robots evaluate these different strategies and demonstrate the advantages of the CA model in affordance learning, especially when learning from limited size data sets. We revisit the notion of object affordances, an idea that speaks to an object's functional properties more than its class label. We study the problem of spatially localizing affordances in the form of 2D segmentation masks annotated with discrete affordance labels. For example, we use affordance masks to denote on what surfaces a person sits, grabs, and looks at when interacting with a variety of everyday objects (such as chairs, bikes, and TVs). We introduce such a functionally-annotated dataset derived from the PASCAL VOC benchmark and empirically evaluate several approaches for predicting such functionally-relevant object regions. We compare "blind" approaches that ignore image data, bottom-up approaches that reason about local surface layout, and top-down approaches that reason about structural constraints between surfaces regions of objects. We show that the difficulty of functional region prediction varies considerably across objects, and that in general, top-down functional object models do well, though there is much room for improvement. Reasoning about objects and their affordances is a fundamental problem for visual intelligence. Most of the previous work casts this problem as a classification task where separate classifiers are trained to label objects, recognize attributes, or assign affordances. In this work, we consider the problem of object affordance reasoning using a knowledge base representation. Diverse information of objects are first harvested from images and other meta-data sources. We then learn a knowledge base (KB) using a Markov Logic Network (MLN). Given the learned KB, we show that a diverse set of visual inference tasks can be done in this unified framework without training separate classifiers, including zero-shot affordance prediction and object recognition given human poses. The ability to perceive possible interactions with the environment is a key capability of task-guided robotic agents. An important subset of possible interactions depends solely on the objects of interest and their position and orientation in the scene. We call these object-based interactions 0-order affordances and divide them among non-hidden and hidden whether the current configuration of an object in the scene renders its affordance directly usable or not. Conversely to other works, we propose that detecting affordances that are not directly perceivable increase the usefulness of robotic agents with manipulation capabilities, so that by appropriate manipulation they can modify the object configuration until the seeked affordance becomes available. In this paper we show how 0-order affordances depending on the geometry of the objects and their pose can be learned using a supervised learning strategy on 3D mesh representations of the objects allowing the use of the whole object geometry. Moreover, we show how the learned affordances can be detected in real scenes obtained with a low-cost depth sensor like the Microsoft Kinect through object recognition and 6D0F pose estimation and present results for both learning on meshes and detection on real scenes to demonstrate the practical application of the presented approach.