Carnegie Mellon University
Semantic Segmentation for Terrain Roughness Estimation Using Data Autolabeled with a Custom Roughness Metric
Traditional methods for off-road terrain estimation use some type of learning network to predict hand labeled classes of terrain such as short grass, tall grass, dirt, and trees. Other methods of learning which can give more detailed, but stilldiscrete classes, use on board sensors to measure the terrain roughness, and then predict the terrain type. There also exists [...]
Carnegie Mellon University
Robust Soft-Matter Robotic Materials
Abstract: Emerging applications in wearable computing, human-machine interaction, and soft robotics will increasingly rely on new soft-matter technologies. These soft-matter technologies are considered inherently safe as they are primarily composed of intrinsically soft materials---elastomers, gels, and fluids. These materials provide a method for creating soft-matter counterparts to traditionally rigid devices that exhibit the mechanical compliance [...]
Carnegie Mellon University
Automated Design of Manipulators For In-Hand Tasks
Grasp planning and motion synthesis for dexterous manipulation tasks are traditionally done given a pre-existing kinematic model for the robotic hand. In this paper, we introduce a framework for automatically designing hand topologies best suited for manipulation tasks given high level objectives as input. Our goal is to ultimately design a program that is able [...]
Relating First-person and Third-person Videos
Abstract: Thanks to the availability and increasing popularity of wearable devices such as GoPro cameras, smart phones and glasses, we have access to a plethora of videos captured from the first person perspective. Capturing the world from the perspective of one's self, egocentric videos bear characteristics distinct from the more traditional third-person (exocentric) videos. In [...]
Learning Neural Parsers with Deterministic Differentiable Imitation Learning
Abstract: In this work, we explore the problem of learning to decompose spatial tasks into segments, as exemplified by the problem of a painting robot covering a large object. Inspired by the ability of classical decision tree algorithms to construct structured partitions of their input spaces, we formulate the problem of decomposing objects into segments [...]
Carnegie Mellon University
Integrating Structure with Deep Reinforcement and Imitation Learning
Most deep reinforcement and imitation learning methods are data-driven and do not utilize the underlying structure of the problem. While these methods have achieved great success on many challenging tasks, several key problems such as generalization, data efficiency and compositionality remain open. Utilizing problem structure in the form of architecture design, priors, domain knowledge etc. may [...]
Carnegie Mellon University
Learning Reactive Flight Control Policies: from LIDAR measurements to Actions
Abstract The end goal of a reactive flight control pipeline is to output control commands based on local sensor inputs. Classical state estimation and control algorithms break down this problem by first estimating the robot’s velocity and then computing a roll and pitch command based on that velocity. However, this approach is not robust in [...]
Carnegie Mellon University
Transparency in Deep Reinforcement Learning Networks
In the recent years there has been a growing interest in the field of Explainability for machine learning models in general and deep learning in particular. This is because, deep learning based approaches have made tremendous progress in the field of computer vision, reinforcement learning, language related domains and are being increasingly used in application areas [...]
Carnegie Mellon University
Geometric approaches to motion planning for two classes of low-Reynolds number swimmers
Microrobots have the potential to impact many areas of medicine such as microsurgery, targeted drug delivery and minimally invasive sensing. Just like microorganisms themselves, microrobots developed for these applications need to swim in a low-Reynolds number regime which warrants locomotive strategies that differ from their macroscopic counterparts. To this end, Purcell’s three-link planar swimmer has [...]
Carnegie Mellon University
Autonomous 3D Reconstruction in Underwater Unstructured Scenes
Abstract Reconstruction of marine structures such as pilings underneath piers presents a plethora of interesting challenges. It is one of those tasks better suited to a robot due to harsh underwater environments. Underwater reconstruction typically involves human operators remotely controlling the robot to predetermined way-points based on some prior knowledge of the location and model [...]