MSR Thesis Defense
MSR Student / Research Assistant
Robotics Institute,
Carnegie Mellon University

Autonomous Sensor Insertion and Exchange for Cornstalk Monitoring Robot

Newell-Simon Hall 4305

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 [...]

MSR Thesis Defense
MSR Student / Research Associate I
Robotics Institute,
Carnegie Mellon University

Multi-Resolution Informative Path Planning for Small Teams of Robots

GHC 4405

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 [...]

MSR Thesis Defense
MSR Student
Robotics Institute,
Carnegie Mellon University

Vision-Language Models for Hand-Object Interaction Prediction

Rashid Auditorium - 4401 Gates and Hillman Centers

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 [...]

MSR Thesis Defense
PhD Student
Robotics Institute,
Carnegie Mellon University

Stabilizing Reinforcement Learning in Differentiable Multiphysics Simulation

GHC 4405

Abstract: Recent advances in GPU-based parallel simulation have enabled practitioners to collect large amounts of data and train complex control policies using deep reinforcement learning (RL), on commodity GPUs. However, such successes for RL in robotics have been limited to tasks sufficiently simulated by fast rigid-body dynamics. Simulation techniques for soft bodies are comparatively several [...]