Robotic Climbing for Extreme Terrain Exploration
Abstract: Climbing robots can investigate scientifically valuable sites that are inaccessible to conventional rovers due to steep terrain features. Robots equipped with microspine grippers are particularly well-suited to ascending rocky cliff faces, but existing designs are either large and slow, or limited to relatively flat surfaces such as buildings. We have developed a novel free-climbing [...]
MSR Thesis Talk: Rohan Zeng
Title: Spectral Unmixing and Mapping of Coral Reef Benthic Cover Abstract: Coral reefs are important to the global ecosystem and the local communities and wildlife that rely on the habitat they create. However, coral reefs are also in critical and rapid decline: reefs have degraded over recent decades and what remains is at increasing risk [...]
Generative modeling: from 3D scenes to fields and manifold
Abstract: In this keynote talk, we delve into some of our progress on generative models that are able to capture the distribution of intricate and realistic 3D scenes and fields. We explore a formulation of generative modeling that optimizes latent representations for disentangling radiance fields and camera poses, enabling both unconditional and conditional generation of 3D [...]
MSR Thesis Talk: Ashwin Misra
Title: Learn2Plan: Learning variable ordering heuristics for scalable task planning Abstract: Traditional approaches to planning attempt to transform a system into a goal state by applying specific actions in a specific order. In these methods, there is an exponential search space due to considering many possible actions at every decision point. Hierarchical Task Networks use incremental [...]
MSR Thesis Talk: Andrew Jong
Title: Robot Information Gathering for Dynamic Systems in Wildfire Scenarios Abstract: The monitoring of complex dynamic systems, such as those encountered in disaster response, search and rescue, wildlife conservation, and environmental monitoring, presents the fundamental challenge of how to track efficiently with limited resources and partial observability. This thesis presents algorithms and techniques for robotic [...]
Carnegie Mellon University
Visual Dataset Pipeline: From Curation to Long-Tail Learning
Abstract: Computer vision models have proven to be tremendously capable of recognizing and detecting several real-world objects: cars, people, pets. These models are only possible due to a meticulous pipeline where a task and application is first conceived followed by an appropriate dataset curation that collects and labels all necessary data. Commonly, studies are focused [...]
MSR Thesis Talk: Erin Wong
Title: Edge Detection by Centimeter Scale Low-Cost Mobile Robots Abstract: In Search and Rescue (SaR) efforts after natural disasters like earthquakes, the primary focus is to find and rescue people in building rubble. These rescue efforts could put first responders at risk and are slow due to the unstable nature of the environment. Robotic solutions [...]
Multi-Objective Ergodic Search for Dynamic Information Maps
Abstract: Robotic explorers are essential tools for gathering information about regions that are inaccessible to humans. For applications like planetary exploration or search and rescue, robots use prior knowledge about the area to guide their search. Ergodic search methods find trajectories that effectively balance exploring unknown regions and exploiting prior information. In many search based [...]
Observing Assistance Preferences via User-controlled Arbitration in Shared Control
Abstract: What factors influence people’s preferences for robot assistance during human-robot collaboration tasks? Answering this question can help roboticists formalize definitions of assistance that lead to higher user satisfaction and increased user acceptance of assistive technology. Often in human robot collaboration literature, we see assistance paradigms that aim to optimize task success metrics and/or measures [...]
RI Faculty Business Meeting
Meeting for RI Faculty. Discussions include various department topics, policies, and procedures. Generally meets weekly.
Sparse-view 3D in the Wild
Abstract: Reconstructing 3D scenes and objects from images alone has been a long-standing goal in computer vision. We have seen tremendous progress in recent years, capable of producing near photo-realistic renderings from any viewpoint. However, existing approaches generally rely on a large number of input images (typically 50-100) in order to compute camera poses and [...]
Safely Influencing Humans in Human-Robot Interaction
Abstract: Robots are becoming more common in industrial manufacturing because of their speed and precision on repetitive tasks, but they lack the flexibility of human collaborators. In order to take advantage of both humans’ and robots’ abilities, we investigate how to improve the efficiency of human-robot collaborations by making sure that robots both 1. stay [...]
Inductive Biases for Learning Long-Horizon Manipulation Skills
Abstract: Enabling robots to execute temporally extended sequences of behaviors is a challenging problem for learned systems, due to the difficulty of learning both high-level task information and low-level control. In this talk, I will discuss three approaches that we have developed to address this problem. Each of these approaches centers on an inductive bias [...]
Estimating Robustness using Proxies
ABSTRACT: This talk covers some of our recent explorations on estimating the robustness of black-box machine learning models across data subpopulations. In other words, if a trained model is uniformly accurate across different types of inputs, or if there are significant performance disparities affecting the different subpopulations. Measuring such a characteristic is fairly straightforward if [...]
Analogy-Forming Transformers for Few-Shot 3D Parsing
Abstract: How do we build agents that can fast generalize to novel scenarios given only a single example? In this talk, I will present analogy-forming transformers, a semi-parametric model that segments 3D object scenes by retrieving related memories and predicting analogous part structures for the input. This enables a single neural network to continually learn [...]