Search Results for author:
Found 9 papers, 1 papers with code
Temporal Multimodal Multivariate Learning
We introduce temporal multimodal multivariate learning, a new family of decision making models that can indirectly learn and transfer online information from simultaneous observations of a probability distribution with more than one peak or more than one outcome variable from one time stage to another.
Lunar Rover Localization Using Craters as Landmarks
At the end of each drive, a ground-in-the-loop (GITL) interaction is used to get a position update from human operators in a more global reference frame, by matching images or local maps from onboard the rover to orbital reconnaissance images or maps of a large region around the rover's current position.
MLNav: Learning to Safely Navigate on Martian Terrains
We present MLNav, a learning-enhanced path planning framework for safety-critical and resource-limited systems operating in complex environments, such as rovers navigating on Mars.
Machine Learning Based Path Planning for Improved Rover Navigation (Pre-Print Version)
Enhanced AutoNav (ENav), the baseline surface navigation software for NASA's Perseverance rover, sorts a list of candidate paths for the rover to traverse, then uses the Approximate Clearance Evaluation (ACE) algorithm to evaluate whether the most highly ranked paths are safe.
Risk-Averse Planning Under Uncertainty
We consider the problem of designing policies for partially observable Markov decision processes (POMDPs) with dynamic coherent risk objectives.
Co-training for Policy Learning
We study the problem of learning sequential decision-making policies in settings with multiple state-action representations.
Learning to Search via Retrospective Imitation
We study the problem of learning a good search policy for combinatorial search spaces.
Mixed Strategy for Constrained Stochastic Optimal Control
We found that the same result holds for stochastic optimal control problems with continuous state and action spaces. Furthermore, we show the randomization of control input can result in reduced cost when the optimization problem is nonconvex, and the cost reduction is equal to the duality gap.
Probabilistic Planning for Continuous Dynamic Systems under Bounded Risk
The second capability is essential for the planner to solve problems with a continuous state space such as vehicle path planning.
