activity
20172023
most citedUltra-fast, programmable, and electronics-free soft robots enabled by snapping metacaps

58 citations · 88 across the 6 of their papers we have counts for

collaborators

6 papers

cs.RO2023★ 10 cited

Learning Complex Motion Plans using Neural ODEs with Safety and Stability Guarantees

Farhad Nawaz, Tianyu Li, Nikolai Matni +1

We propose a Dynamical System (DS) approach to learn complex, possibly periodic motion plans from kinesthetic demonstrations using Neural Ordinary Differential Equations (NODE). To…

cs.CV2022★ 10 cited

A Combined Approach Toward Consistent Reconstructions of Indoor Spaces Based on 6D RGB-D Odometry and KinectFusion

Nadia Figueroa, Haiwei Dong, Abdulmotaleb El Saddik

We propose a 6D RGB-D odometry approach that finds the relative camera pose between consecutive RGB-D frames by keypoint extraction and feature matching both on the RGB and depth i…

cs.RO2022★ 58 cited

Ultra-fast, programmable, and electronics-free soft robots enabled by snapping metacaps

Lishuai Jin, Yueying Yang, Bryan O. Torres Maldonado +4

Soft robots have a myriad of potentials because of their intrinsically compliant bodies, enabling safe interactions with humans and adaptability to unpredictable environments. Howe…

cs.RO2022★ 3 cited

Temporal Logic Imitation: Learning Plan-Satisficing Motion Policies from Demonstrations

Yanwei Wang, Nadia Figueroa, Shen Li +2

Learning from demonstration (LfD) has succeeded in tasks featuring a long time horizon. However, when the problem complexity also includes human-in-the-loop perturbations, state-of…

cs.RO2020★ 2 cited

RoCUS: Robot Controller Understanding via Sampling

Yilun Zhou, Serena Booth, Nadia Figueroa +1

As robots are deployed in complex situations, engineers and end users must develop a holistic understanding of their behaviors, capabilities, and limitations. Some behaviors are di…

cs.LG2017★ 5 cited

Transform-Invariant Non-Parametric Clustering of Covariance Matrices and its Application to Unsupervised Joint Segmentation and Action Discovery

Nadia Figueroa, Aude Billard

In this work, we tackle the problem of transform-invariant unsupervised learning in the space of Covariance matrices and applications thereof. We begin by introducing the Spectral…