activity
20192023
most citedEV-Catcher: High-Speed Object Catching Using Low-latency Event-based Neural Networks

26 citations · 30 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2023

Instance-Agnostic Geometry and Contact Dynamics Learning

Mengti Sun, Bowen Jiang, Bibit Bianchini +2

This work presents an instance-agnostic learning framework that fuses vision with dynamics to simultaneously learn shape, pose trajectories, and physical properties via the use of…

cs.RO2023

Enabling Large-scale Heterogeneous Collaboration with Opportunistic Communications

Fernando Cladera, Zachary Ravichandran, Ian D. Miller +3

Multi-robot collaboration in large-scale environments with limited-sized teams and without external infrastructure is challenging, since the software framework required to support…

cs.RO202326 cited

EV-Catcher: High-Speed Object Catching Using Low-latency Event-based Neural Networks

Ziyun Wang, Fernando Cladera Ojeda, Anthony Bisulco +6

Event-based sensors have recently drawn increasing interest in robotic perception due to their lower latency, higher dynamic range, and lower bandwidth requirements compared to sta…

cs.RO20224 cited

Stronger Together: Air-Ground Robotic Collaboration Using Semantics

Ian D. Miller, Fernando Cladera, Trey Smith +2

In this work, we present an end-to-end heterogeneous multi-robot system framework where ground robots are able to localize, plan, and navigate in a semantic map created in real tim…

cs.RO2021

Fast Footstep Planning on Uneven Terrain Using Deep Sequential Models

Hersh Sanghvi, Camillo Jose Taylor

One of the fundamental challenges in realizing the potential of legged robots is generating plans to traverse challenging terrains. Control actions must be carefully selected so th…

cs.CV2019

DFuseNet: Deep Fusion of RGB and Sparse Depth Information for Image Guided Dense Depth Completion

Shreyas S. Shivakumar, Ty Nguyen, Ian D. Miller +3

In this paper we propose a convolutional neural network that is designed to upsample a series of sparse range measurements based on the contextual cues gleaned from a high resoluti…