Robots need to understand their environment to perform their task. If it is possible to pre-program a visual scene analysis process in closed environments, robots operating in an open environment would benefit from the ability to learn it through their interaction with their environment. This ability furthermore opens the way to the acquisition of affordances maps in which the action capabilities of the robot structure its visual scene understanding. We propose an approach to build such affordances maps by relying on an interactive perception approach and an online classification. In the proposed formalization of affordances, actions and effects are related to visual features, not objects, and they can be combined. We have tested the approach on three action primitives and on a real PR2 robot.
These works @cite_37 @cite_21 @cite_2 are close to the work presented in this paper. They gather in a single study affordance learning, online learning, exploration process, and interactive perception. The affordance map of Kim and Sukhatme @cite_21 is close to our relevance map by the way they both segment interesting elements for the agent, but exploration and learning were conducted in simulation only, in simple environments and setups, and only one affordance was learnt. The study proposed by @cite_2 can learn several affordances in simulation, but it was tested in reality with only one action. The approach proposed in this article is based on similar principles but it allows the system to learn relevance maps relative to several affordances in more complex and realistic environments, in real world-experiments.
@cite_37 @cite_21 @cite_2
2099816052 805016335 1599132676
The concept of affordances, as proposed by J.J. Gibson, refers to the relationship between the organism and its environment and has become popular in autonomous robot control. The learning of affordances in autonomous robots, however, typically requires a large set of training data obtained from the interactions of the robot with its environment. Therefore, the learning process is not only time-consuming, and costly but is also risky since some of the interactions may inflict damage on the robot. In this paper, we study the learning of traversability affordance on a mobile robot and investigate how the number of interactions required can be minimized with minimial degradation on the learning process. Specifically, we propose a two step learning process which consists of bootstrapping and curiosity-based learning phases. In the bootstrapping phase, a small set of initial interaction data are used to find the relevant perceptual features for the affordance, and a support vector machine (SVM) classifier is trained. In the curiosity-driven learning phase, a curiosity band around the decision hyperplane of the SVM is used to decide whether a given interaction opportunity is worth exploring or not. Specifically, if the output of the SVM for a given percept lies within curiosity band, indicating that the classifier is not so certain about the hypothesized effect of the interaction, the robot goes ahead with the interaction, and skips if not. Our studies within a physics-based robot simulator show that the robot can achieve better learning with the proposed curiosity-driven learning method for a fixed number of interactions. The results also show that, for optimum performance, there exists a minimum number of initial interactions to be used for bootstrapping. Finally, the trained classifier with the proposed learning method was also successfully tested on the real robot. We describe a technique to build an affordance map interactively for robotic tasks. Affordances are predicted by a trained classifier using geometric features extracted from objects. Based on 2D occupancy grid, a Markov Random Field (MRF) model builds an affordance map with relational affordance with neighboring cells. The quality of the affordance map is refined by sequences of interactive manipulations selected from the model to yield the highest reduction in uncertainty. Recently, the aspect of visual perception has been explored in the context of Gibson's concept of affordances [1] in various ways. We focus in this work on the importance of developmental learning and the perceptual cueing for an agent's anticipation of opportunities for interaction, in extension to functional views on visual feature representations. The concept for the incremental learning of abstract from basic affordances is presented in relation to learning of complex affordance features. In addition, the work proposes that the originally defined representational concept for the perception of affordances - in terms of using either motion or 3D cues - should be generalized towards using arbitrary visual feature representations. We demonstrate the learning of causal relations between visual cues and associated anticipated interactions by reinforcement learning of predictive perceptual states. We pursue a recently presented framework for cueing and recognition of affordance-based visual entities that obviously plays an important role in robot control architectures, in analogy to human perception. We experimentally verify the concept within a real world robot scenario by learning predictive visual cues using reinforcement signals, proving that features were selected for their relevance in predicting opportunities for interaction.