RI40: Past, Present, and Future
Please plan to join us on Friday, October 25, 2019 as we celebrate 40 years of people, robots, and innovation! 40 years ago Carnegie Mellon University’s Robotics Institute opened its doors with the dream of ushering in a new age of thinking robots. During the ensuing decades, we have experienced many research successes in intelligent [...]
RI40 Seminar: From Direct-Drive to SuperLimb Bionics
In 1980-81 the first Direct-Drive robot was developed at the CMU Robotics Institute. After almost 40 years, Direct-Drive has a renewed interest in the leg robotics community. Robotic legs powered by direct-drive or low gear-reduction motors can better interact with the ground and absorb impacts. In this seminar I will talk about robot design in [...]
Tartan AUV: A Dive into Carnegie Mellon’s RoboSub Team
Abstract: Founded last year, Tartan AUV is Carnegie Mellon’s undergraduate underwater robotics team which competes annually in the RoboSub competition. RoboSub teams must design, build, and test autonomous underwater vehicles that compete each August to complete tasks related to underwater navigation, object detection and manipulation, and acoustic beacon localization. In this talk we will provide [...]
DNA and gammaPNA in programmable nanomaterials for sensing, robotics and manufacturing
Abstract: When programmable nanomaterials are used in conjunction with rapid microfabrication techniques like two photon polymerization, it becomes possible to rapidly prototype microstructures with nanoscale components. In this research presentation I introduce DNA nanotechnology using a commonly used simple nanotube motif, and I will illustrate how nucleic acid nanotubes can be used in sensing, robotics [...]
Towards Lightweight Real-time Hand Reconstruction in Challenging
Abstract: Humans naturally use their hands to interact and communicate with their surroundings. Reconstructing these complex and dexterous hand interactions enables sign-language recognition and translation, better assistive robots, and more immersive human-computer interaction (e.g. for AR and VR). To make hand reconstruction usable for the aforementioned applications and to a wide set of users, the [...]
Soft Robotics challenges: Design, Fabrication, Control, and Motion Planning
Abstract: More and more robots of the future will be soft. A soft body can absorb impact forces from collisions with obstacles, making robots suitable for unpredictable environments and safe for human-robot interaction. However, widespread application of soft robotics in daily life, business, and consumer products have not yet been achieved, because established robotic technologies, [...]
Hybrid Methods for the Integration of Heterogeneous Multimodal Biomedical Data
Abstract: The prevalence of smartphones and wearable devices for health monitoring and widespread use of electronic health records have led to a surge in heterogeneous multimodal healthcare data, collected at an unprecedented scale. My research focuses on developing machine learning techniques that learn salient representations of multimodal, heterogeneous data for biomedical predictive models. The first [...]
Carnegie Mellon University
Online and Consistent Occupancy Grid Mapping
Abstract: Actively exploring and mapping an unknown environment requires integration of both simultaneous localization and mapping (SLAM) and path planning methods. Path planning relies on a map that contains free and occupied space information and is efficient to query, while the role of SLAM is to keep the map consistent as new measurements are continuously [...]
Carnegie Mellon University
A Planning Framework for Persistent, Multi-UAV Coverage with Global Deconfliction
Abstract: Planning for multi-robot coverage seeks to determine collision-free paths for a fleet of robots, enabling them to collectively observe points of interest in an environment. Persistent coverage is a variant of traditional coverage where coverage-levels in the environment decay over time. Thus, robots have to continuously revisit parts of the environment to maintain a [...]
The Robots are Coming – to your Farm! AKA: Autonomous and Intelligent Robots in Unstructured Field Environments
Abstract: What if a team of collaborative autonomous robots grew your food for you? In this talk, I will discuss some key advances in robotics, machine learning, and autonomy that will one day enable teams of small robots to grow food for you in your backyard in a fundamentally more sustainable way than modern mega-farms! [...]
Carnegie Mellon University
‘Unboxing’ anomaly detection and panoptic segmentation
Abstract: Panoptic segmentation is a recent problem in computer vision that attempts to classify each pixel in an image according to its semantic and instance label (accomplishing both semantic segmentation and instance segmentation respectively). Most existing panoptic and instance segmentation methods run a detection-first pipeline, where a bounding box is placed around an object and [...]
Carnegie Mellon University
Self-Supervised Learning on Mobile Robots Using Acoustics, Vibration, and Visual Models to Build Rich Semantic Terrain Maps
Abstract: Humans and robots would benefit from having rich semantic maps of the terrain in which they operate. Mobile robots equipped with sensors and perception software could build such maps as they navigate through a new environment. This information could then be used by humans or robots for better localization and path planning, as well [...]
Self-Driving Cars & AI: Transforming our Cities and our Lives
Abstract: Recent algorithmic and hardware improvements resulted in several success stories in the field of Artificial Intelligence (AI) which impact our daily lives. However, despite its ubiquity, AI is only just starting to make advances in what may arguably have the largest societal impact thus far, the nascent field of autonomous driving. At Uber ATG, [...]
Carnegie Mellon University
Transfers Between Multiple Service Robots
Abstract: With the deployment of more robots, human-robot interaction will no longer be limited to a one-to-one interaction between a user and a robot. Instead, users will likely have to interact with multiple robots, simultaneously or sequentially, throughout their day to receive services and complete different tasks. In this thesis proposal, I am proposing joint [...]
Carnegie Mellon University
Machine Learning Parallelism Could Be Adaptive, Composable and Automated
Abstract: In recent years, the pace of innovations in the fields of machine learning has accelerated. To cope with the sheer computational complexity of training large ML models on large datasets, researchers in SysML have created algorithms and systems that parallelize ML training and inference over multiple CPUs or GPUs, or even multiple computing nodes [...]