📄 Paper | 🌐 Interactive Webpage
A simple module to represent trajectories using quadratic splines, enabling smooth transitions from movement primitives to distance fields and dynamical systems—all with analytical gradients and PyTorch support.
This project provides a simple and lightweight implementation to convert a trajectory into:
- Movement Primitives (MP)
- Distance Fields (DF)
- Dynamical Systems (DS)
by representing it as a series of concatenated quadratic splines. Thanks to the analytical gradients, it's easy to compute distances and directions at any point around the trajectory.
- ✅ Minimal dependencies (built with PyTorch, no heavy libraries needed)
- ✅ Fully vectorized and parallelizable
- ✅ Supports gradient-based learning, optimization, and control
- ✅ Efficient computation
| File | Description |
|---|---|
data |
Trajectories for testing |
quadratic_spline.py |
Core implementation of spline representation and gradient computation |
run_mp_df_ds.py |
Example: Convert a quadratic spline into distance field, and dynamical system |
run_single_traj.py |
Similar to above, but for a trajectory that represented using discrete points |
run_multiple_traj.py |
Combine and fuse multiple trajectories |
run_LASA.py |
Run experiments on the LASA dataset (requires pylasadataset) |
This code is maintained by Yiming LI and licensed under the MIT License.
Copyright (c) 2025 Idiap Research Institute [email protected]