Mobile robots operating on unstructured terrain must predict which areas of the environment they are able to pass in order to plan feasible paths and to react to unforeseen terrain patterns. We address traversability estimation as a heightmap classification problem: we build a convolutional neural network that, given an image representing the heightmap of a terrain patch, predicts whether the robot will be able to traverse such patch from left to right. The classifier is trained for a specific wheeled robot model (but may implement any other locomotion type such as tracked, legged, snake-like), using simulation data on a variety of procedurally generated training terrains; once trained, the classifier can quickly be applied to patches extracted from unseen large heightmaps, in multiple orientations, thus building oriented traversability maps. We quantitatively validate the approach on real-elevation datasets and implement a path planning approach that employs our traversability estimation.
A second option is to entrust labeling to a human; this could be done in a direct, straightforward way (the robot acquires data, a human marks each input with a traversability label, yielding a training set) @cite_10 , or using strategies to make the process more efficient. For example, in @cite_27 a human draws a path from a source to a target, and the system infers which areas the human purposefully avoided and automatically uses such patches as non-traversable examples.
@cite_27 @cite_10
2949098821 2201912979 1994367471 2111478152
Current learning-based robot grasping approaches exploit human-labeled datasets for training the models. However, there are two problems with such a methodology: (a) since each object can be grasped in multiple ways, manually labeling grasp locations is not a trivial task; (b) human labeling is biased by semantics. While there have been attempts to train robots using trial-and-error experiments, the amount of data used in such experiments remains substantially low and hence makes the learner prone to over-fitting. In this paper, we take the leap of increasing the available training data to 40 times more than prior work, leading to a dataset size of 50K data points collected over 700 hours of robot grasping attempts. This allows us to train a Convolutional Neural Network (CNN) for the task of predicting grasp locations without severe overfitting. In our formulation, we recast the regression problem to an 18-way binary classification over image patches. We also present a multi-stage learning approach where a CNN trained in one stage is used to collect hard negatives in subsequent stages. Our experiments clearly show the benefit of using large-scale datasets (and multi-stage training) for the task of grasping. We also compare to several baselines and show state-of-the-art performance on generalization to unseen objects for grasping. Current model free learning-based robot grasping approaches exploit human-labeled datasets for training the models. However, there are two problems with such a methodology: (a) since each object can be grasped in multiple ways, manually labeling grasp locations is not a trivial task; (b) human labeling is biased by semantics. While there have been attempts to train robots using trial-and-error experiments, the amount of data used in such experiments remains substantially low and hence makes the learner prone to over-fitting. In this paper, we take the leap of increasing the available training data to 40 times more than prior work, leading to a dataset size of 50K data points collected over 700 hours of robot grasping attempts. This allows us to train a Convolutional Neural Network (CNN) for the task of predicting grasp locations without severe overfitting. In our formulation, we recast the regression problem to an 18-way binary classification over image patches. We also present a multi-stage learning approach where a CNN trained in one stage is used to collect hard negatives in subsequent stages. Our experiments clearly show the benefit of using large-scale datasets (and multi-stage training) for the task of grasping. We also compare to several baselines and show state-of-the-art performance on generalization to unseen objects for grasping. As researchers are striving for developing robotic systems able to move into the the wild', the interest towards novel learning paradigms for domain adaptation has increased. In the specific application of semantic place recognition from cameras, supervised learning algorithms are typically adopted. However, once learning has been performed, if the robot is moved to another location, the acquired knowledge may be not useful, as the novel scenario can be very different from the old one. The obvious solution would be to retrain the model updating the robot internal representation of the environment. Unfortunately this procedure involves a very time consuming data-labeling effort at the human side. To avoid these issues, in this paper we propose a novel transfer learning approach for place categorization from visual cues. With our method the robot is able to decide automatically if and how much its internal knowledge is useful in the novel scenario. Differently from previous approaches, we consider the situation where the old and the novel scenario may differ significantly (not only the visual room appearance changes but also different room categories are present). Importantly, our approach does not require labeling from a human operator. We also propose a strategy for improving the performance of the proposed method by fusing two complementary visual cues. Our extensive experimental evaluation demonstrates the advantages of our approach on several sequences from publicly available datasets. In many real world applications we do not have access to fully-labeled training data, but only to a list of possible labels. This is the case, e.g., when learning visual classifiers from images downloaded from the web, using just their text captions or tags as learning oracles. In general, these problems can be very difficult. However most of the time there exist different implicit sources of information, coming from the relations between instances and labels, which are usually dismissed. In this paper, we propose a semi-supervised framework to model this kind of problems. Each training sample is a bag containing multi-instances, associated with a set of candidate labeling vectors. Each labeling vector encodes the possible labels for the instances in the bag, with only one being fully correct. The use of the labeling vectors provides a principled way not to exclude any information. We propose a large margin discriminative formulation, and an efficient algorithm to solve it. Experiments conducted on artificial datasets and a real-world images and captions dataset show that our approach achieves performance comparable to an SVM trained with the ground-truth labels, and outperforms other baselines.