A causal framework to diagnose and fix issues with doors
Abstract: Many animals, such as ravens, (and a fortiori humans) exhibit a great deal of physical intelligence that allows them to solve complex multi-step physical puzzles. This ability indicates an understanding or a faculty to represent causality and mechanisms, understand when something goes wrong, and figure out how to deal with it. As a step [...]
Carnegie Mellon University
Understanding Unbalanced Datasets Through Simple Models and Dataset Exploration
Abstract: Computer vision models have proven to be tremendously capable of recognizing and detecting several classes and objects. They succeed in classes widely ranging in type and scale from humans to cans to pens. However, the best performing classes have abundant examples in large-scale datasets today. In unbalanced datasets, where some categories are seen in [...]
Domain adaptive object detection
Abstract: Recent advances in deep learning have led to the development of accurate and efficient models for object detection. However, learning highly accurate models relies on the availability of large-scale annotated datasets. Due to this, model performance drops drastically when evaluated on label-scarce datasets having visually distinct images. Domain adaptation tries to mitigate this degradation. In [...]
Carnegie Mellon University
Understanding, Exploiting and Improving Inter-view Relationships
Abstract: Multi-view machine learning has garnered substantial attention in various applications over recent years. Many such applications involve learning on data obtained from multiple heterogeneous sources of information, for example, in multi-sensor systems such as self-driving cars, or monitoring intensive care patient vital signs at their bed-side. Learning models for such applications can often benefit [...]
Model-Centric Verification of Artificial Intelligence
Abstract: This work shows how provable guarantees can be used to supplement probabilistic estimates in the context of Artificial Intelligence (AI) systems. Statistical techniques measure the expected performance of a model, but low error rates say nothing about the ways in which errors manifest. Formal verification of model adherence to design specifications can yield certificates [...]
Designing Whisker Sensors to Detect Multiple Mechanical Stimuli for Robotic Applications
Abstract: Many mammals, such as rats and seals, use their whiskers as versatile mechanical sensors to gain precise information about their surroundings. Whisker-inspired sensors on robotic platforms have shown their potential benefit, improving applications ranging from drone navigation to texture mapping. Despite this, there is a gap between the engineered sensors and many of the [...]
Carnegie Mellon University
Human-in-the-loop Control of Mobile Robots
Abstract: Human-in-the-loop control for mobile robots is an important aspect of robot operation, especially for navigation in unstructured environments or in the case of unexpected events. However, traditional paradigms of human-in-the-loop control have relied heavily on the human to provide precise and accurate control inputs to the robot, or reduced the role of the human [...]
Visual Understanding across Semantic Groups, Domains and Devices
Abstract: Deep neural networks often lack generalization capabilities to accommodate changes in the input/output domain distributions and, therefore, are inherently limited by the restricted visual and semantic information contained in the original training set. In this talk, we argue the importance of the versatility of deep neural architectures and we explore it from various perspectives. [...]
Towards Robust Human-Robot Interaction: A Quality Diversity Approach
Abstract: The growth of scale and complexity of interactions between humans and robots highlights the need for new computational methods to automatically evaluate novel algorithms and applications. Exploring the diverse scenarios of interaction between humans and robots in simulation can improve understanding of complex human-robot interaction systems and avoid potentially costly failures in real-world settings. [...]
Carnegie Mellon University
Planning and Execution using Inaccurate Models with Provable Guarantees on Task Completeness
Abstract: Modern planning methods are effective in computing feasible and optimal plans for robotic tasks when given access to accurate dynamical models. However, robots operating in the real world often face situations that cannot be modeled perfectly before execution. Thus, we only have access to simplified but potentially inaccurate models. This imperfect modeling can lead [...]