Planners#
This section documents the planners provided by the project with a focus on planners for robotic motion: path planning, trajectory generation, collision avoidance, and practical considerations such as smoothness and dynamic feasibility.
The repository contains several planner implementations — each has a dedicated page with implementation details and examples. Use the links at the bottom of this page to jump to a specific planner.
Overview#
The embodichain project provides a unified interface for robot trajectory planning, supporting both joint space and Cartesian space interpolation. The main planners include:
MotionGenerator: A unified trajectory planning interface that supports joint/Cartesian interpolation, automatic constraint handling, flexible planner selection, and backend-specific collision-aware planning.
ToppraPlanner: A time-optimal trajectory planner based on the TOPPRA library, supporting joint trajectory generation under velocity and acceleration constraints.
NeuralPlanner (experimental): A learning-based EEF waypoint planner for Franka Panda.
CuroboPlanner (optional): A cuRobo V2 backend that plans on CUDA and supports either CPU or CUDA physics simulation for collision-aware single-arm Cartesian and joint-space planning.
TrajectorySampleMethod: An enumeration for trajectory sampling strategies, supporting sampling by time, quantity, or distance.
These tools can be used to generate smooth and dynamically feasible robot trajectories. Install NVIDIA’s CUDA-matched cuRobo source package separately when collision-aware planning against an explicit cuRobo world is required.
Use NeuralPlanner (experimental) when you have a trained APG checkpoint and need learned EEF waypoint rollout on Franka Panda.