Student Talks
Carnegie Mellon University
Designing Interactive Systems for Community Citizen Science
Abstract: Citizen science forges partnerships between experts and citizens through collaboration and has become a trend in public participation in scientific research over the past decade. Besides this trend, public participation can also contribute to participatory democracy, which empowers citizens to advocate for their local problems. This strategy supports citizens to form a community, increase [...]
Design with Interpretability in Mind: An Alternate Ethos for Data Science
Abstract: The fields of Machine Learning and Data Science generally follow the paradigm that “the ends justify the means”, where improving predictive power of an algorithm is considered of paramount value, even when implemented at the expense of model intelligibility. While accuracy is an important performance metric, interpretability should be a major consideration for many [...]
What can this robot do? Learning Capability Models from Appearance and Experiments
As autonomous robots become increasingly multifunctional and adaptive, it becomes difficult to determine the extent of their capabilities, i.e. the tasks they can perform and their strengths and limitations at these tasks. A robot's appearance can provide cues to its physical as well as cognitive capabilities. We present an algorithm that builds on these cues [...]
Carnegie Mellon University
Visual Learning with Minimal Human Supervision
Abstract: Machine learning models have led to remarkable progress in visual recognition. A key factor driving this progress is the abundance of labeled data. Unfortunately, this reliance on lots of labeled data is also a key limitation in the rapid development and deployment of vision systems. These visual recognition systems show poor performance on concepts [...]
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 [...]
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 [...]