Learning and Control of Hamiltonian System Dynamics We previously developed a port-Hamiltonian dynamics model, which was trained as a neural ordinary differential equation (ODE) neural network and subsequently used for energy-shaping control to achieve stabilization and tracking. Recently we have extended our method in two major directions. In work published at ICRA 2024 (Altawaitan et […]
Efficient Reinforcement Learning for Robotic Control We design efficient reinforcement learning (RL) for generalizing on diverse tasks and environments, as well as for generalizing from simulation to real robots. We specifically made two efforts along this direction: TD-MPC2 Building on our previous effort on TD-MPC, a model-based reinforcement learning algorithm that optimizes local trajectories in […]
Learning Control Barrier Functions for Multi-robot Navigation Learning-based control methods must satisfy safety requirements to be deployed in real-world robotics systems. Control barriers, a potential candidate for standardizing the notion of safety in the learning community, achieve a theoretical guarantee of controller safety via specifying forward-invariant safe regions of the system using Lyapunov theory. We […]
Household Navigation and Manipulation for Everyday Object Rearrangement Tasks We consider the problem of building an assistive robotic system that can help humans in daily household cleanup tasks. Creating such an autonomous system in real-world environments is inherently quite challenging, as a general solution may not suit the preferences of a particular customer. Moreover, such […]
Cognitive Mapping for Indoor Spaces Simultaneous Localization and Mapping (SLAM) is essential for robots to efficiently understand environments, navigate, and interact within spaces. Object-level SLAM, which utilizes sparse data to provide a high-level understanding, faces two main challenges in real-time applications: under-constrained optimization and difficulties in online association. Our work presents a real-time object SLAM […]
