reference my paper

examples/MANN/velMANNAutoregressive.ipynb

@@ -2,12 +2,13 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "808dd4f3",
    "metadata": {},
    "source": [
     "# velMANNAutoregressive\n",
     "This notebook contains the code showing how to use velMANNAutoregressive to generate a walking trajectory for the ergoCub humanoid robot. \n",
     "\n",
-    "If you are interested in some details of the algorithm, please refer to the paper: [ADHERENT: Learning Human-like Trajectory Generators for Whole-body Control of Humanoid Robots](https://github.com/ami-iit/paper_viceconte_2021_ral_adherent).\n",
+    "If you are interested in some details of the algorithm, please refer to the paper: [Trajectory Generation with Physics-Informed Learning and Drift Mitigation](https://github.com/ami-iit/paper_delia_2024_ral_physics-informed_trajectory_generation).\n",
     "\n",
     "## Import all the required packages\n",
     "In this section we import all the required packages. In particular, we need `iDynTree` to correctly visualize the robot, `numpy` to perform some basic operations, `manifpy` to perform some basic operations on manifolds, `resolve_robotics_uri_py` to correctly locate the `ergoCub` model. Finally `bipedal_locomotion_framework` implements the MANN network."

examples/MANN/velMANNTrajectoryGenerator.ipynb

@@ -7,7 +7,7 @@
     "# velMANNTrajectoryGenerator\n",
     "This notebook contains the code showing how to use velMANNTrajectoryGenerator to generate a walking trajectory for the ergoCub humanoid robot. \n",
     "\n",
-    "If you are interested in some details of the algorithm, please refer to the paper: [ADHERENT: Learning Human-like Trajectory Generators for Whole-body Control of Humanoid Robots](https://github.com/ami-iit/paper_viceconte_2021_ral_adherent).\n",
+    "If you are interested in some details of the algorithm, please refer to the paper: [Trajectory Generation with Physics-Informed Learning and Drift Mitigation](https://github.com/ami-iit/paper_delia_2024_ral_physics-informed_trajectory_generation).\n",
     "\n",
     "Differently from the `velMANNAutoregressive`, `velMANNTrajectoryGenerator` class can be used to plan a trajectory that can be fed into an MPC. Indeed it is possible to sample the contact and extract a `ContactPhaseList` that can be used to initialize the MPC. Moreover the `velMANNTrajectoryGenerator` can be reset to any point of the trajectory, allowing to replan at will.\n",
     "\n",

src/ML/include/BipedalLocomotion/ML/velMANNTrajectoryGenerator.h

@@ -60,11 +60,13 @@ struct velMANNTrajectoryGeneratorOutput
 /**
  * velMANNTrajectoryGenerator is a class that uses velMANNAutoregressive to generate a kinematically
  * feasible trajectory for humanoid robots. The planner will generate a trajectory which duration is
- * equal to `slow_down_factor * time_horizon` seconds.
+ * equal to `slow_down_factor * time_horizon` seconds. This class differs from the MANNTrajectoryGenerator
+ * class in that the input and output features of the learned model are velocity-based rather than
+ * position-based. The postprocessing, using the 3D velocity output features of the learned model, allows
+ * more modularity in the trajectory generation. More details are available in the paper mentioned below.
  * @subsection mann_trajectory_generator velMANN trajectory generator
  * The diagram illustrates the utilization of the velMANNAutoregressive within the
  * velMANNTrajectoryGenerator class.
- * <img src="https://user-images.githubusercontent.com/16744101/239922103-1f23e08b-0091-475a-8879-61af66399c62.png" alt="mann_trajectory_generator">
  * To initialize the generator, the user needs to set the initial
  * state using the velMANNTrajectoryGenerator::setInitialState method. The
  * velMANNTrajectoryGeneratorInput, provided by the user, serves as the input for the
@@ -79,10 +81,9 @@ struct velMANNTrajectoryGeneratorOutput
  * velMANNAutoregressive at the designated merge point. Every time the velMANNAutoregressive::advance()
  * function is invoked by the velMANNTrajectoryGenerator, the autoregressive state is stored for future
  * reference.
- * @note The implementation of the class follows the work presented in "P. M. Viceconte et al.,
- * ADHERENT: Learning Human-like Trajectory Generators for Whole-body Control of Humanoid Robots,
- * in IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 2779-2786, April 2022,
- * doi: 10.1109/LRA.2022.3141658." https://doi.org/10.1109/LRA.2022.3141658
+ * @note The implementation of the class follows the work presented in "Trajectory Generation with
+ * Physics-Informed Learning and Drift Mitigation", available at
+ * https://github.com/ami-iit/paper_delia_2024_ral_physics-informed_trajectory_generation.
  */
 class velMANNTrajectoryGenerator
     : public System::Advanceable<velMANNTrajectoryGeneratorInput, velMANNTrajectoryGeneratorOutput>