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Found 5 papers, 1 papers with code
Closed-Form Minkowski Sum Approximations for Efficient Optimization-Based Collision Avoidance
Motion planning methods for autonomous systems based on nonlinear programming offer great flexibility in incorporating various dynamics, objectives, and constraints.
Learn Proportional Derivative Controllable Latent Space from Pixels
Recent advances in latent space dynamics model from pixels show promising progress in vision-based model predictive control (MPC).
Autonomously Navigating a Surgical Tool Inside the Eye by Learning from Demonstration
We show that the network can reliably navigate a needle surgical tool to various desired locations within 137 microns accuracy in physical experiments and 94 microns in simulation on average, and generalizes well to unseen situations such as in the presence of auxiliary surgical tools, variable eye backgrounds, and brightness conditions.
Combining Neural Networks and Tree Search for Task and Motion Planning in Challenging Environments
This paper investigates the ability of neural networks to learn both LTL constraints and control policies in order to generate task plans in complex environments.
Robotics
Sample Complexity Bounds for Iterative Stochastic Policy Optimization
This paper is concerned with robustness analysis of decision making under uncertainty.
