Carnegie Mellon University
Search-based Robust Motion Planning under Uncertainty Guided by Multiple Heuristics
Abstract: Motion planning has achieved a great success in many robotic applications but still suffers in the real world under ample uncertainty. For example, manipulation involves interaction with unstructured and stochastic environments, which results in motion uncertainty. Perception that provides understanding of the environment is also not perfect, which in turn leads to sensing uncertainty. [...]
Carnegie Mellon University
Robust State Estimation for Micro Aerial Vehicles
Title: Robust State Estimation for Micro Aerial Vehicles Autonomous robots provide excellent tools for information gathering in a wide variety of domains, from environmental management to infrastructure inspection and search and rescue. Micro aerial vehicles, in particular, offer a high degree of mobil- ity that can further their effectiveness in such environments. Deployment of aerial [...]
Deep Reinforcement Learning with skill library: Learning and exploration with temporal abstractions using coarse approximate dynamics models
Reinforcement learning is a computational approach to learn from interaction. However, learning from scratch using reinforcement learning requires exorbitant number of interactions with the environment even for simple tasks. One way to alleviate the problem is to reuse previously learned skills as done by humans. This thesis provides frameworks and algorithms to build and reuse [...]
Deformable models meet deep learning: supervised and unsupervised approaches
Abstract: In this talk I will be presenting recent work on combining ideas from deformable models with deep learning. I will start by describing DenseReg and DensePose, two recently introduced systems for establishing dense correspondences between 2D images and 3D surface models ``in the wild'', namely in the presence of background, occlusions, and multiple objects. [...]
Building Scalable Framework and Environment of Reinforcement Learning
Abstract: Deep Reinforcement Learning (DRL) has made strong progress in many tasks that are traditionally considered to be difficult, such as complete information games, navigation, architecture search, etc. Although the basic principle of DRL is quite simple and straightforward, to make it work often requires substantially more samples with more computational resource, compared to traditional [...]
Learning Deep Multimodal Features for Reliable and Comprehensive Scene Understanding
Abstract Robust scene understanding is a critical and essential task for autonomous navigation. This problem is heavily influenced by changing environmental conditions that take place throughout the day and across seasons. In order to learn models that are impervious to these factors, mechanisms that intelligently fuse features from complementary modalities and spectra have to be [...]
Scene Understanding
Abstract: Accurate and efficient scene understanding is a fundamental task in a variety of computer vision applications including autonomous driving, human-machine interaction, and robot navigation. Reducing computational complexity and memory use is important to minimize response time and power consumption for portable devices such as robots and virtual/augmented devices. Also, it is beneficial for vehicles [...]
Carnegie Mellon University
Robot Design for Everyone: Computational Tools that Democratize the Design of Robots
Abstract: A grand vision in robotics is that of a future wherein robots are integrated in daily human life just as smart phones and computers are today. Such pervasive integration of robots would require faster design and manufacturing of robots that cater to individual needs. For instance, people would be able to obtain customized smart [...]
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 [...]
Carnegie Mellon University
Wire Detection, Reconstruction, and Avoidance for Unmanned Aerial Vehicles
Abstract Thin objects, such as wires and power lines are one of the most challenging obstacles to detect and avoid for UAVs, and are a cause of numerous accidents each year. This thesis makes contributions in three areas of this domain: wire segmentation, reconstruction, and avoidance. Pixelwise wire detection can be framed as a binary [...]
Carnegie Mellon University
Multi-Robot Routing and Scheduling with Spatio-Temporal And Ordering Constraints
Abstract We consider the problem of allocation and routing a fleet of robots to service a given set of locations while minimizing makespan. The service start times for the locations are subject to AND/OR type precedence constraints. Spatio-temporal constraints prohibit certain states from all feasible schedules where a state is defined as a tuple of [...]
The Art of Robotics: Toward a Holistic Approach
I arrived at the Robotics Institute two years ago looking for a good project, something tangible and preferably related to legged locomotion. Instead, I met Matt Mason and started to think about the big picture, ask the big questions. What is manipulation? What is robotics? What makes robotics particularly hard? To answer these questions, I [...]