A new optimization framework for robot motion planning

robot-motion-planningconvex-optimizationgraph-searchmit-csailrobotics

Abstraction: MIT GCS algorithm merges graph search and convex optimization for robot navigation

Key points:

Graphs of Convex Sets (GCS) Trajectory Optimization from MIT CSAIL: combines graph search (discrete path finding) with convex optimization (continuous trajectory smoothing)
Scales to 14+ dimensions; outperforms sampling-based methods (e.g., RRT) in speed and path quality in complex environments
Demo: two robotic arms holding a mug navigated around a shelf, synchronizing motion without dropping objects
Simulation: quadrotor flew through a building avoiding trees and navigating precise door/window angles
Published on cover of Science Robotics (2023); foundational 2021 paper in SIAM Journal on Optimization
Open research direction: extending GCS to contact-rich manipulation (pushing/sliding objects)

Connections: Mit Csail · Robot Motion Planning · Convex Optimization · Graph Search