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.

This paper proposes an end-to-end self-supervised feature representation network named Attentive Task-Net or AT-Net for video-based task imitation. The proposed AT-Net incorporates a novel multi-level spatial attention module to highlight spatial features corresponding to the intended task demonstrated by the expert. The neural connections in AT-Net ensure the relevant information in the demonstration is amplified and the irrelevant information is suppressed while learning task-specific feature embeddings. This is achieved by a weighted combination of multiple intermediate feature maps of the input image at different stages of the CNN pipeline. The weights of the combination are given by the compatibility scores, predicted by the attention module for respective feature maps. The AT-Net is trained using a metric learning loss which aims to decrease the distance between the feature representations of concurrent frames from multiple view points and increase the distance between temporally consecutive frames. The AT-Net features are then used to formulate a reinforcement learning problem for task imitation. Through experiments on the publicly available Multi-view pouring dataset, it is demonstrated that the output of the attention module highlights the task-specific objects while suppressing the rest of the background. The efficacy of the proposed method is further validated by qualitative and quantitative comparison with a state-of-the-art technique along with intensive ablation studies. The proposed method is implemented to imitate a pouring task where an RL agent is learned with the AT-Net in Gazebo simulator. Our findings show that the AT-Net achieves 6.5% decrease in alignment error along with a reduction in the number of training iterations by almost 155k over the state-of-the-art while satisfactorily imitating the intended task.

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.

Visual place recognition (VPR) - the act of recognizing a familiar visual place - becomes difficult when there is extreme environmental appearance change or viewpoint change. Particularly challenging is the scenario where both phenomena occur simultaneously, such as when returning for the first time along a road at night that was previously traversed during the day in the opposite direction. While such problems can be solved with panoramic sensors, humans solve this problem regularly with limited field-of-view vision and without needing to constantly turn around. In this paper, we present a new depth- and temporal-aware visual place recognition system that solves the opposing viewpoint, extreme appearance-change visual place recognition problem. Our system performs sequence-to-single frame matching by extracting depth-filtered keypoints using a state-of-the-art depth estimation pipeline, constructing a keypoint sequence over multiple frames from the reference dataset, and comparing these keypoints to the keypoints extracted from a single query image. We evaluate the system on a challenging benchmark dataset and show that it consistently outperforms state-of-the-art techniques. We also develop a range of diagnostic simulation experiments that characterize the contribution of depth-filtered keypoint sequences with respect to key domain parameters including the degree of appearance change and camera motion.

This paper presents a new GAN-based deep learning framework for estimating absolute scale awaredepth and ego motion from monocular images using a completely unsupervised mode of learning.The proposed architecture uses two separate generators to learn the distribution of depth and posedata for a given input image sequence. The depth and pose data, thus generated, are then evaluated bya patch-based discriminator using the reconstructed image and its corresponding actual image. Thepatch-based GAN (or PatchGAN) is shown to detect high frequency local structural defects in thereconstructed image, thereby improving the accuracy of overall depth and pose estimation. Unlikeconventional GANs, the proposed architecture uses a conditioned version of input and output of thegenerator for training the whole network. The resulting framework is shown to outperform all existing deep networks in this field and beating the current state-of-the-art method by 8.7% in absoluteerror and 5.2% in RMSE metric. To the best of our knowledge, this is first deep network based modelto estimate both depth and pose simultaneously using a conditional patch-based GAN paradigm.The efficacy of the proposed approach is demonstrated through rigorous ablation studies and exhaustive performance comparison on the popular KITTI outdoor driving dataset.

This paper presents a deep network based unsupervised visual odometry system for 6-DoF camera pose estimation and finding dense depth map for its monocular view. The proposed network is trained using unlabeled binocular stereo image pairs and is shown to provide superior performance in depth and ego-motion estimation compared to the existing state-of-the-art. This is achieved by introducing a novel objective function and training the network using temporally alligned sequences of monocular images. The objective function is based on the Charbonnier penalty applied to spatial and bi-directional temporal reconstruction losses. The overall novelty of the approach lies in the fact that the proposed deep framework combines a disparity-based depth estimation network with a pose estimation network to obtain absolute scale-aware 6-DoF camera pose and superior depth map. According to our knowledge, such a framework with complete unsupervised end-to-end learning has not been tried so far, making it a novel contribution in the field. The effectiveness of the approach is demonstrated through performance comparison with the state-of-the-art methods on KITTI driving dataset.