Affordances are a fundamental concept in robotics since they relate available actions for an agent depending on its sensory-motor capabilities and the environment. We present a novel Bayesian deep network to detect affordances in images, at the same time that we quantify the distribution of the aleatoric and epistemic variance at the spatial level. We adapt the Mask-RCNN architecture to learn a probabilistic representation using Monte Carlo dropout. Our results outperform the state-of-the-art of deterministic networks. We attribute this improvement to a better probabilistic feature space representation on the encoder and the Bayesian variability induced at the mask generation, which adapts better to the object contours. We also introduce the new Probability-based Mask Quality measure that reveals the semantic and spatial differences on a probabilistic instance segmentation model. We modify the existing Probabilistic Detection Quality metric by comparing the binary masks rather than the predicted bounding boxes, achieving a finer-grained evaluation of the probabilistic segmentation. We find aleatoric variance in the contours of the objects due to the camera noise, while epistemic variance appears in visual challenging pixels.
翻译:在机器人中,负担是一个基本概念,因为其影响取决于感知运动能力和环境的代理器的可用动作。我们展示了一个新颖的Bayesian深度网络,以检测图像中的富饶度,与此同时,我们量化空间层面的显性与显性差异分布;我们改造Mask-RCNNN 结构以学习使用蒙特卡洛辍学的概率代表法。我们的成果优于确定性网络的先进水平。我们将这一改进归因于在编码器和遮罩生成过程中产生的贝氏变异性空间的更好概率特征代表,从而更好地适应对象轮廓。我们还采用了基于概率的新的显性面具质量测量,以显示概率感应变分解模型的语性和空间差异。我们通过比较二元面罩而不是预测的捆绑框来修改现有的稳定性检测质量度,从而实现对抗变性分解器生成的精确度的精确度评估。我们发现,在相近的摄像器中,我们发现视觉变异性,而视觉变异性则显示相。</s>