Revolutionary Motion Generation with cuRobo: CUDA Accelerated Robotics

Revolutionary Motion Generation with cuRobo: CUDA Accelerated Robotics

Table of Contents

1. Introduction
2. Motion Generation for Robots
   1. Path Planning Challenges
   2. Graph Planning
   3. Trajectory Optimization
3. Introducing Kur Robo
   1. GPU Acceleration for Motion Generation
   2. Collision-Free Minimum Jerk Trajectories
4. How Kur Robo Works
   1. Example Problem
   2. Inverse Kinematics
   3. Trajectory Optimization
   4. Fine-Tuning Optimization
5. The Implementation of Kur Robo
   1. Robot Geometry Representation
   2. Custom CUDA Kernels
   3. Numerical Optimization Solvers
   4. Collision Computation Implementation
6. Benchmarking and Metrics
   1. Comparison with Artic and TROB
   2. Path Length and Trajectory Time
   3. Fast Planning Time
7. Applications and Use Cases
   1. Motion Generation for Real Robots
   2. Improving Rearrangement Planning
   3. Integration with NV Blocks
8. Conclusion
9. Resources

🤖 Motion Generation for Robots

Robots are increasingly being used in applications where pre-programmed path planning is not sufficient. These robots need to generate new motion plans on the fly to account for changing environments and constraints. Traditional motion generation approaches, such as graph planning and trajectory optimization, have limitations in terms of planning time, optimality, and smoothness.

Path Planning Challenges

In complex domains, finding the shortest path from a graph planner can take several seconds. It is also difficult to encode additional cost functions, such as minimum jerk, into the planning process. On the other HAND, trajectory optimization methods can get stuck in local minima and are reliant on the initial seed for good results.

Graph Planning

Graph planning involves building a graph of nodes connected by collision-free edges that represent feasible motion paths for the robot. This approach is well-studied in the field of robotics, with planners like RRT and BIT* being commonly used. However, graph planning alone cannot achieve smooth and optimal trajectories.

Trajectory Optimization

Trajectory optimization addresses the limitations of graph planning by performing numerical optimization over an initial trajectory seed. This optimization can incorporate additional cost functions, resulting in smoother and more efficient trajectories. However, trajectory optimization is prone to getting stuck in local minima and can take several seconds to plan a motion for a manipulator.

🚀 Introducing Kur Robo

Kur Robo is a GPU-accelerated motion generation toolkit that solves the challenges of traditional motion generation approaches. By leveraging the power of GPUs, Kur Robo can generate collision-free minimum jerk trajectories within milliseconds.

GPU Acceleration for Motion Generation

Kur Robo implements the entire motion generation stack on the GPU, enabling optimization over many Parallel seeds to find the best trajectory. This parallel processing capability allows for quick exploration of the solution space and results in faster planning times.

Collision-Free Minimum Jerk Trajectories

Kur Robo combines the strengths of graph planning and trajectory optimization to generate smooth and collision-free motion paths. It uses a graph planner on the GPU to build a graph with collision-free edges, which serves as the seed for trajectory optimization. By performing trajectory optimization from this seed, Kur Robo achieves shorter and smoother paths compared to traditional methods.

🤔 How Kur Robo Works

To understand how Kur Robo works, let's walk through an example problem. First, inverse kinematics is performed to find goal configurations that map to the desired end effector pose. Then, trajectory optimization is conducted using seeds generated by linear interpolation from the start state to the goal configurations. After trajectory optimization, a fine-tuning optimization step is employed to minimize jerk.

Kur Robo implements several core computational blocks as custom CUDA kernels, including the kinematics function and numerical optimization solvers. It also incorporates collision computation using metrics from previous Papers, enhancing its success rate in finding collision-free paths.

⚙️ The Implementation of Kur Robo

In the implementation of Kur Robo, the robot geometry is represented using spheres, and the kinematics function maps joint configurations to the position of the end effector. Most of the core computational blocks are implemented as custom CUDA kernels, taking advantage of the parallel processing capabilities of the GPU.

Numerical optimization solvers, such as LBFGS and parallel line search schemes, are also implemented with custom CUDA kernels. Additionally, Kur Robo incorporates a sphere-cuboid sign distance function for collision avoidance terms in optimization.

The implementation of Kur Robo leverages several NVIDIA technologies, including mesh sign distance through NVIDIA's Warp and depth image-based sign distance queries through NVBlocks. This allows for efficient computation and optimization of collision-free trajectories.

To make Kur Robo accessible to users, all the components have been integrated within PyTorch, providing a Python front end. This integration not only makes it easier to implement new cost terms and optimization problems but also allows users to take advantage of the vast ecosystem of PyTorch.

🔍 Benchmarking and Metrics

Kur Robo has undergone extensive benchmarking with other motion planning libraries like Artic and TROB. The results show that Kur Robo outperforms these libraries in various metrics, including path length and trajectory time.

Compared to Artic, Kur Robo reduces the average path length by 11% and the path length at the 98th percentile by 28%. It also generates trajectories that reach the goal 1.6 times faster than trajectories generated with TROB at the 98th percentile.

The planning time of Kur Robo is significantly faster than both Artic and TROB, with an average planning time of 50 milliseconds and a 75th percentile planning time of 30 milliseconds. Kur Robo achieves this quick planning time by leveraging the parallel processing capabilities of GPUs.

🌐 Applications and Use Cases

Kur Robo's motion generation capabilities have wide-ranging applications in various domains. It enables real robots to quickly generate motion plans on Nvidia Jetson devices, making it suitable for real-time applications.

In rearrangement planning tasks, Kur Robo improves efficiency and generates more natural-looking motions compared to standard planners. It also provides APIs for attaching objects to the gripper, which is crucial in pick and place tasks.

Integration with NVBlocks allows Kur Robo to react to obstacles perceived from a depth camera, enabling safe and efficient navigation in cluttered environments. It avoids collisions with both static and moving obstacles and can adapt to dynamically changing scenarios.

✨ Conclusion

Kur Robo is a cutting-edge GPU-accelerated motion generation toolkit that overcomes the limitations of traditional approaches. It combines the strengths of graph planning and trajectory optimization to generate collision-free minimum jerk trajectories within milliseconds. With its fast planning times, optimality, and smoothness, Kur Robo is revolutionizing motion generation for robots.

📚 Resources

For more details and examples of Kur Robo, visit our website at www.kurrobo.com.