145 citations · 811 across the 39 of their papers we have counts for
10 papers · 1 filter
Space Objects Maneuvering Prediction via Maximum Causal Entropy Inverse Reinforcement Learning
Bryce Doerr, Richard Linares, Roberto Furfaro
Inverse Reinforcement Learning (RL) can be used to determine the behavior of Space Objects (SOs) by estimating the reward function that an SO is using for control. The approach dis…
Atmospheric Density Uncertainty Quantification for Satellite Conjunction Assessment
David J. Gondelach, Richard Linares
Conjunction assessment requires knowledge of the uncertainty in the predicted orbit. Errors in the atmospheric density are a major source of error in the prediction of low Earth or…
Six Degree-of-Freedom Body-Fixed Hovering over Unmapped Asteroids via LIDAR Altimetry and Reinforcement Meta-Learning
Brian Gaudet, Richard Linares, Roberto Furfaro
We optimize a six degrees of freedom hovering policy using reinforcement meta-learning. The policy maps flash LIDAR measurements directly to on/off spacecraft body-frame thrust com…
Real-Time Thermospheric Density Estimation Via Two-Line-Element Data Assimilation
David Gondelach, Richard Linares
Inaccurate estimates of the thermospheric density are a major source of error in low Earth orbit prediction. To improve orbit prediction, real-time density estimation is required.…
Seeker based Adaptive Guidance via Reinforcement Meta-Learning Applied to Asteroid Close Proximity Operations
Brian Gaudet, Richard Linares, Roberto Furfaro
Current practice for asteroid close proximity maneuvers requires extremely accurate characterization of the environmental dynamics and precise spacecraft positioning prior to the m…
Combining Parameter Identification and Trajectory Optimization: Real-time Planning for Information Gain
Keenan Albee, Monica Ekal, Rodrigo Ventura +1
Robotic systems often operate with uncertainties in their dynamics, for example, unknown inertial properties. Broadly, there are two approaches for controlling uncertain systems: d…
Reinforcement Learning for Angle-Only Intercept Guidance of Maneuvering Targets
Brian Gaudet, Roberto Furfaro, Richard Linares
We present a novel guidance law that uses observations consisting solely of seeker line of sight angle measurements and their rate of change. The policy is optimized using reinforc…
Adaptive Guidance and Integrated Navigation with Reinforcement Meta-Learning
Brian Gaudet, Richard Linares, Roberto Furfaro
This paper proposes a novel adaptive guidance system developed using reinforcement meta-learning with a recurrent policy and value function approximator. The use of recurrent netwo…
Adaptive Guidance with Reinforcement Meta-Learning
Brian Gaudet, Richard Linares
This paper proposes a novel adaptive guidance system developed using reinforcement meta-learning with a recurrent policy and value function approximator. The use of recurrent netwo…
Learning Accurate Extended-Horizon Predictions of High Dimensional Trajectories
Brian Gaudet, Richard Linares, Roberto Furfaro
We present a novel predictive model architecture based on the principles of predictive coding that enables open loop prediction of future observations over extended horizons. There…