← Search

Samuel Tesfazgi

3 accepted papers

2025

Learning Geometrically-Informed Lyapunov Functions with Deep Diffeomorphic RBF Networks

AISTATS 2025poster

The practical deployment of learning-based autonomous systems would greatly benefit from tools that flexibly obtain safety guarantees in the form of certificate functions from data. While the geometrical properties of such certificate functions are well understood, synthesizing them using machine le…

Cited by 0SourcecodeScholar
2024

Data-driven Force Observer for Human-Robot Interaction with Series Elastic Actuators using Gaussian Processes

IROS 2024poster

Ensuring safety and adapting to the user’s behavior are of paramount importance in physical human-robot interaction. Thus, incorporating elastic actuators in the robot’s mechanical design has become popular, since it offers intrinsic compliance and additionally provide a coarse estimate for the inte…

Cited by 0SourceScholar
2023

Vision-Based Uncertainty-Aware Motion Planning Based on Probabilistic Semantic Segmentation

RA-L 2023

For safe operation, a robot must be able to avoid collisions in uncertain environments. Existing approaches for motion planning under uncertainties often assume parametric obstacle representations and Gaussian uncertainty, which can be inaccurate. While visual perception can deliver a more accurate

Cited by 8SourceScholar