Search Results for author:
Found 8 papers, 5 papers with code
Learning to Identify Graphs from Node Trajectories in Multi-Robot Networks
The graph identification problem consists of discovering the interactions among nodes in a network given their state/feature trajectories.
Lie Group Forced Variational Integrator Networks for Learning and Control of Robot Systems
In this paper, we introduce a new structure-preserving deep learning architecture, the Lie group Forced Variational Integrator Network (LieFVIN), capable of learning controlled Lagrangian or Hamiltonian dynamics on Lie groups, either from position-velocity or position-only data.
LEMURS: Learning Distributed Multi-Robot Interactions
This paper presents LEMURS, an algorithm for learning scalable multi-robot control policies from cooperative task demonstrations.
Adaptive Control of SE(3) Hamiltonian Dynamics with Learned Disturbance Features
Adaptive control is a critical component of reliable robot autonomy in rapidly changing operational conditions.
Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control
This paper proposes a Hamiltonian formulation over the SE(3) manifold of the structure of a neural ordinary differential equation (ODE) network to approximate the dynamics of a rigid body.
Autonomous Navigation in Unknown Environments with Sparse Bayesian Kernel-based Occupancy Mapping
This paper focuses on online occupancy mapping and real-time collision checking onboard an autonomous robot navigating in a large unknown environment.
Robotics
Safe Robot Navigation in Cluttered Environments using Invariant Ellipsoids and a Reference Governor
This paper considers the problem of safe autonomous navigation in unknown environments, relying on local obstacle sensing.
Systems and Control Robotics Systems and Control
Autonomous Navigation in Unknown Environments using Sparse Kernel-based Occupancy Mapping
This paper focuses on real-time occupancy mapping and collision checking onboard an autonomous robot navigating in an unknown environment.
