Robust Reinforcement Learning for Safety Critical Applications via Curricular Learning
Abstract: Reinforcement Learning (RL) presents great promises for autonomous agents. However, when using robots in a safety critical domain, a system has to be robust enough to be deployed in real life. For example, the robot should be able to perform across different scenarios it will encounter. The robot should avoid entering undesirable and irreversible [...]
Practical Challenges and Recent Advances in Data Attribution
Abstract: Data plays an increasingly crucial role in both the performance and the safety of AI models. Data attribution is an emerging family of techniques aimed at quantifying the impact of individual training data points on a model trained on them, which has found data-centric applications such as training data curation, instance-based explanation, and copyright [...]
Spatial Reasoning and Semantic Representations for Intelligent Multi-Robot Exploration and Navigation
Abstract: Autonomous robot exploration is widely applied in areas such as search and rescue, environmental monitoring, and structural inspection. Multi-robot exploration has garnered significant attention in the robotics research community, as it enables faster task completion and greater coverage than a single robot can achieve. However, it presents unique challenges: behavior coordination is complex, communication [...]
Autonomous Sensor Insertion and Exchange for Cornstalk Monitoring Robot
Abstract: Interactive sensors are an important component of robotic systems but often require manual replacement due to wear and tear. Automating this process can enhance system autonomy and facilitate long-term deployment. We developed an autonomous sensor exchange and maintenance system for an agriculture crop monitoring robot that inserts a nitrate sensor into cornstalks. A novel [...]
Leveraging Sense of Agency to Improve the Experience of Control Over Assistive Robots
Abstract: In an age of autonomous driving and robotics, we are increasingly engaging with robots that deploy autonomous assistance. Cognitive science and human-computer interaction literature tells us that, when we apply autonomy in assistive settings, we are often augmenting the user's sense of agency over the system. Sense of agency is a phenomenon from cognitive [...]
Artificial Intelligence in Support of Emergency Care in the Field
Abstract: Medical emergencies demand rapid and accurate interventions to save lives. Severe injuries often require surgical care within the first 60 minutes when timely action significantly improves survival rates. However, limited resources, remote locations, and unpredictable conditions often obstruct access to advanced medical care during this critical period. This thesis focuses on developing a medical [...]
Efficient Synthetic Data Generation and Utilization for Action Recognition and Universal Avatar Generation
Abstract: Human-centered computer vision technology relies heavily on large, diverse datasets, but collecting data from human subjects is time-consuming, labor-intensive, and raises privacy concerns. To address these challenges, researchers are increasingly using synthetic data to augment real-world datasets. This thesis explores efficient methods for generating and utilizing synthetic data to train human-based computer vision models. [...]
Multi-Resolution Informative Path Planning for Small Teams of Robots
Abstract: Unmanned aerial vehicles can increase the efficiency of information gathering applications . A key challenge is balancing the search across multiple locations of varying importance while determining the best sensing altitude, given each agent's finite operation time. In this work, we present a multi-resolution informative path planning approach for small teams of unmanned aerial [...]
Communication-Efficient Active Reconstruction using Self-Organizing Gaussian Mixture Models
Abstract: For the multi-robot active reconstruction task, this thesis proposes using Gaussian mixture models (GMMs) as the map representation that enables multiple downstream tasks: high-fidelity static scene reconstruction, communication-efficient map sharing, and safe informative planning. A new method called Self-Organizing Gaussian mixture modeling (SOGMM) is proposed that estimates the model complexity (i.e., number of Gaussian [...]
RI Faculty Business Meeting
Meeting for RI Faculty. Agenda was sent via a calendar invite.
From Lab to Launch
Bio: Nathan Michael is Shield AI’s Chief Technology Officer and a former Associate Research Professor in the Robotics Institute of Carnegie Mellon University (CMU). At CMU, Nathan was the Director of the Resilient Intelligent Systems Lab, a research lab dedicated to improving the performance and reliability of artificially intelligent and autonomous systems that operate in [...]
Vision-Language Models for Hand-Object Interaction Prediction
Abstract: How can we predict future interaction trajectories of human hands in a scene given high-level colloquial task specifications in the form of natural language? In this paper, we extend the classic hand trajectory prediction task to two tasks involving explicit or implicit language queries. Our proposed tasks require extensive understanding of human daily activities [...]
Robotics Institute Winter Party
All Robotics Institute Faculty. Staff, Students, and Visitors are invited to attend this event. Please join us for food, beverages, and casual conversation with colleagues. A calendar invite including details will be sent closer to the event.
RI Faculty Business Meeting
Meeting for RI Faculty. Agenda was sent via a calendar invite.