activity
20172026
most citedLearning Implicit Priors for Motion Optimization

23 citations · 46 across the 8 of their papers we have counts for

collaborators

12 papers

cs.RO2026

ZEST: Zero-shot Embodied Skill Transfer for Athletic Robot Control

Jean Pierre Sleiman, He Li, Alphonsus Adu-Bredu +25

Achieving robust, human-like whole-body control on humanoid robots for agile, contact-rich behaviors remains a central challenge, demanding heavy per-skill engineering and a brittl…

cs.RO2023

V-STRONG: Visual Self-Supervised Traversability Learning for Off-road Navigation

Sanghun Jung, JoonHo Lee, Xiangyun Meng +2

Reliable estimation of terrain traversability is critical for the successful deployment of autonomous systems in wild, outdoor environments. Given the lack of large-scale annotated…

cs.RO2023

Demonstrating HOUND: A Low-cost Research Platform for High-speed Off-road Underactuated Nonholonomic Driving

Sidharth Talia, Matt Schmittle, Alexander Lambert +3

Off-road autonomy, crucial for applications such as search-and-rescue, agriculture, and planetary exploration, poses unique problems due to challenging terrains, as well as due to…

cs.RO2023★ 1 cited

TerrainNet: Visual Modeling of Complex Terrain for High-speed, Off-road Navigation

Xiangyun Meng, Nathan Hatch, Alexander Lambert +11

Effective use of camera-based vision systems is essential for robust performance in autonomous off-road driving, particularly in the high-speed regime. Despite success in structure…

cs.RO2022★ 23 cited

Learning Implicit Priors for Motion Optimization

Julen Urain, An T. Le, Alexander Lambert +3

In this paper, we focus on the problem of integrating Energy-based Models (EBM) as guiding priors for motion optimization. EBMs are a set of neural networks that can represent expr…

cs.RO2021

Stein Variational Probabilistic Roadmaps

Alexander Lambert, Brian Hou, Rosario Scalise +2

Efficient and reliable generation of global path plans are necessary for safe execution and deployment of autonomous systems. In order to generate planning graphs which adequately…