From virtual demonstration to real-world manipulation using LSTM and MDN

Robots assisting the disabled or elderly must perform complex manipulation tasks and must adapt to the home environment and preferences of their user. Learning from demonstration is a promising choice, that would allow the non-technical user to teach the robot different tasks. However, collecting demonstrations in the home environment of a disabled user is time consuming, disruptive to the comfort of the user, and presents safety challenges. It would be desirable to perform the demonstrations in a virtual environment. In this paper we describe a solution to the challenging problem of behavior transfer from virtual demonstration to a physical robot. The virtual demonstrations are used to train a deep neural network based controller, which is using a Long Short Term Memory (LSTM) recurrent neural network to generate trajectories. The training process uses a Mixture Density Network (MDN) to calculate an error signal suitable for the multimodal nature of demonstrations. The controller learned in the virtual environment is transferred to a physical robot (a Rethink Robotics Baxter). An off-the-shelf vision component is used to substitute for geometric knowledge available in the simulation and an inverse kinematics module is used to allow the Baxter to enact the trajectory. Our experimental studies validate the three contributions of the paper: (1) the controller learned from virtual demonstrations can be used to successfully perform the manipulation tasks on a physical robot, (2) the LSTM+MDN architectural choice outperforms other choices, such as the use of feedforward networks and mean-squared error based training signals and (3) allowing imperfect demonstrations in the training set also allows the controller to learn how to correct its manipulation mistakes

Feature extraction using MPEG-CDVS and Deep Learning with application to robotic navigation and image classification

The main contributions of this thesis are the evaluation of MPEG Compact Descriptor for Visual Search in the context of indoor robotic navigation and the introduction of a new method for training Convolutional Neural Networks with applications to object classification. The choice for image descriptor in a visual navigation system is not straightforward. Visual descriptors must be distinctive enough to allow for correct localisation while still offering low matching complexity and short descriptor size for real-time applications. MPEG Compact Descriptor for Visual Search is a low complexity image descriptor that offers several levels of compromises between descriptor distinctiveness and size. In this work, we describe how these trade-offs can be used for efficient loop-detection in a typical indoor environment. We first describe a probabilistic approach to loop detection based on the standard’s suggested similarity metric. We then evaluate the performance of CDVS compression modes in terms of matching speed, feature extraction, and storage requirements and compare them with the state of the art SIFT descriptor for five different types of indoor floors. During the second part of this thesis we focus on the new paradigm to machine learning and computer vision called Deep Learning. Under this paradigm visual features are no longer extracted using fine-grained, highly engineered feature extractor, but rather using a Convolutional Neural Networks (CNN) that extracts hierarchical features learned directly from data at the cost of long training periods. In this context, we propose a method for speeding up the training of Convolutional Neural Networks (CNN) by exploiting the spatial scaling property of convolutions. This is done by first training a pre-train CNN of smaller kernel resolutions for a few epochs, followed by properly rescaling its kernels to the target’s original dimensions and continuing training at full resolution. We show that the overall training time of a target CNN architecture can be reduced by exploiting the spatial scaling property of convolutions during early stages of learning. Moreover, by rescaling the kernels at different epochs, we identify a trade-off between total training time and maximum obtainable accuracy. Finally, we propose a method for choosing when to rescale kernels and evaluate our approach on recent architectures showing savings in training times of nearly 20% while test set accuracy is preserved

Deep Forward and Inverse Perceptual Models for Tracking and Prediction

We consider the problems of learning forward models that map state to high-dimensional images and inverse models that map high-dimensional images to state in robotics. Specifically, we present a perceptual model for generating video frames from state with deep networks, and provide a framework for its use in tracking and prediction tasks. We show that our proposed model greatly outperforms standard deconvolutional methods and GANs for image generation, producing clear, photo-realistic images. We also develop a convolutional neural network model for state estimation and compare the result to an Extended Kalman Filter to estimate robot trajectories. We validate all models on a real robotic system.Comment: 8 pages, International Conference on Robotics and Automation (ICRA) 201

SEGCloud: Semantic Segmentation of 3D Point Clouds

3D semantic scene labeling is fundamental to agents operating in the real world. In particular, labeling raw 3D point sets from sensors provides fine-grained semantics. Recent works leverage the capabilities of Neural Networks (NNs), but are limited to coarse voxel predictions and do not explicitly enforce global consistency. We present SEGCloud, an end-to-end framework to obtain 3D point-level segmentation that combines the advantages of NNs, trilinear interpolation(TI) and fully connected Conditional Random Fields (FC-CRF). Coarse voxel predictions from a 3D Fully Convolutional NN are transferred back to the raw 3D points via trilinear interpolation. Then the FC-CRF enforces global consistency and provides fine-grained semantics on the points. We implement the latter as a differentiable Recurrent NN to allow joint optimization. We evaluate the framework on two indoor and two outdoor 3D datasets (NYU V2, S3DIS, KITTI, Semantic3D.net), and show performance comparable or superior to the state-of-the-art on all datasets.Comment: Accepted as a spotlight at the International Conference of 3D Vision (3DV 2017