The ability to apply a previously-learned skill (e.g., pushing) to a new task (context or object) is an important requirement for new-age robots. An attempt is made to solve this problem in this paper by proposing a deep meta-imitation learning framework comprising of an attentive-embedding net-work and a control network, capable of learning a new task in an end-to-end manner while requiring only one or a few visual demonstrations. The feature embeddings learnt by incorporating spatial attention is shown to provide higher embedding and control accuracy compared to other state-of-the-art methods such as TecNet [7] and MIL [4]. The interaction between the embedding and the control networks is improved by using multiplicative skip-connections and is shown to overcome the overfitting of the trained model. The superiority of the proposed model is established through rigorous experimentation using a publicly available dataset and a new dataset created using PyBullet [36]. Several ablation studies have been carried out to justify the design choices.

In this paper, we propose an unsupervised deep learning framework with Bayesian inference for improving the accuracy of per-pixel depth prediction from monocular RGB images. The proposed framework predicts confidence map along with depth and pose information for a given input image. The depth hypotheses from previous frames are propagated forward and fused with the depth hypothesis of the current frame by using Bayesian inference mechanism. The ground truth information required for training the confidence map prediction is constructed using image reconstruction loss thereby obviating the need for explicit ground truth depth information used in supervised methods. The resulting unsupervised framework is shown to outperform the existing state-of-the-art methods for depth prediction on the publicly available KITTI outdoor dataset. The usefulness of the proposed framework is further established by demonstrating a real-world robotic pick-and-place application where the pose of the robot end-effector is computed using the depth predicted from an eye-in-hand monocular camera. The design choices made for the proposed framework is justified through extensive ablation studies.