PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Strategy and Skill Learning for Physics-based Table Tennis Animation

Abstract: Recent advancements in physics-based character animation leverage deep learning to generate agile and natural motion, enabling characters to execute movements such as backflips, boxing, and tennis. However, reproducing the selection and use of diverse motor skills in dynamic environments to solve complex tasks, as humans do, still remains a challenge. We present a strategy [...]

PhD Thesis Proposal
PhD Student
Robotics Institute,
Carnegie Mellon University

Getting Optimization layers to play well with Deep Networks: Numerical methods and Architectures

NSH 4305

Abstract: Many real-world challenges, from robotic control to resource management, can be effectively formulated as optimization problems. Recent advancements have focused on incorporating these optimization problems as layers within deep learning pipelines, enabling the explicit inclusion of auxiliary constraints or cost functions, which is crucial for applications such as enforcing physical laws, ensuring safety constraints, [...]