Sampling-based motion planning methods for manipulators in crowded environments often
suffer from expensive collision checking and high sampling complexity, which make them …

We develop a computationally efficient learning-based forward–backward stochastic
differential equations (FBSDE) controller for both continuous and hybrid dynamical (HD) …

To achieve safe legged locomotion, it is crucial to generate motion in real-time considering
various constraints in robots and environments. In this study, we propose a lightweight real …

Robotic planning algorithms direct agents to perform actions within diverse environments to
accomplish a task. Large Language Models (LLMs) like PaLM 2, GPT-3.5, and GPT-4 have …

A Control Barrier Function (CBF)-based motion planning algorithm is proposed. The
algorithm explores an unknown environment to reach a target point, providing velocity …

Control Barrier Functions (CBFs) when paired with Quadratic Programming offer an efficient
way to generate safety-critical controllers. In this paper, we utilize CBFs for guiding multiple …

In this work, we propose a trajectory optimization approach for robot navigation in cluttered
3D environments. We represent the robot's geometry as a semialgebraic set defined by …

The objective of this article is to develop real-time controllers for positioning multi-agent
autonomous platforms (specifically drones) without any collisions. The framework is divided …

The visibility (possibly partial) of some image features is crucial to a broad class of visual
servoing-based control. In this letter, we consider the setting of image-based visual servoing …

Generating motion for robots that interact with objects of various shapes is a complex
challenge, further complicated when the robot's own geometry and multiple desired …