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Rafid Saif

Alumni
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Publications

Imitation Learning via Discrete and Rhythmic Primitive Composition
Rafid Saif
Auke Ijspeert
Hsiu-Chin Lin
Long-horizon robotic manipulation is inherently composed of semantically meaningful sub-tasks. While Movement Primitives (MPs) can model the… (see more)se components, their composition typically relies on manual engineering or sample-inefficient reinforcement learning. Existing supervised methods are limited by the lack of MP labels in standard datasets and often fail to generalize across discrete and rhythmic movements. We propose Neural Dynamic Composition (NDC), a supervised framework to compose Dynamic Movement Primitives (DMPs) using only raw trajectories for supervision, given a single example of each primitive. Our approach exploits parallel combinations of DMPs as a differentiable relaxation of categorical primitive selection, and utilizes a warp-invariant loss formulation to identify primitives despite spatial or temporal variations. A staged training curriculum is employed to learn the selection policy and DMP parameters, unifying discrete and rhythmic movements in one framework. Evaluated on 2D handwriting and 3D robotic tasks, our method outperforms baselines on most tasks and metrics. An ablation study demonstrates how each design choice contributes to the system's performance.
2026-10-10
arXiv (preprint)
doi.org
arxiv.org
Designing Experimental Setup Emulating Log-Loader Manipulator and Implementing Anti-Sway Trajectory Planner
Iman Jebellat
George Sideris
Rafid Saif
Inna Sharf
Forestry machines are not easily accessible for experimentation or demonstration of research results. These mobile robots are massive, very … (see more)expensive, and require a large outdoor space and permits to operate. These factors hinder conducting experiments on real forestry robots. Thus, it is essential to design experimental setups utilizing easily accessible robots in indoor labs that can effectively replicate the behavior of interest of a forestry machine. We design a setup to resemble a log-loader crane and grapple motions using a Kinova Jaco2 arm by manufacturing a specialized end-effector to attach passively to the Jaco2 arm. Passively attached grapple causes undesirable sway, which is problematic and dangerous in forestry. To address the sway problem, we employ dynamic programming to develop an anti-sway motion planner, and validate its performance for different point-to-point maneuvers in our experimental setup. We also repeat each experiment at least 6 times to ensure the repeatability and reliability of the experiments. The experimental results showcase the excellent sway-damping performance of our planner and also the very good repeatability of our experiments.
2024-12-31
IEEE International Conference on Robotics and Automation (published)
doi.org