Alexander Lambert — accepted AI-conference papers

2024

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

Sidharth Talia, Matthew Schmittle, Alexander Lambert, Alexander Spitzer, Christoforos Mavrogiannis, Siddhartha Srinivasa

RSS 2024poster

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 the risk involved in testing or deploying such systems. Accessible platforms have the potential to widen the field to a…

Cited by 5 Source ↗PDF ↗Scholar ↗

2024

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

Sanghun Jung, JoonHo Lee, Xiangyun Meng, Byron Boots, Alexander Lambert

ICRA 2024poster

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 datasets for off-road navigation, strictly-supervised learning approaches remain limited in their generalization abilit…

Cited by 32 Source ↗Scholar ↗

2023

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

Xiangyun Meng, Nathan Hatch, Alexander Lambert, Anqi Li, Nolan Wagener, Matthew Schmittle +8

RSS 2023poster

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 structured, on-road settings, current end-to-end approaches for scene prediction have yet to be successfully adapted for complex…

Cited by 62 Source ↗PDF ↗Scholar ↗

2022

Learning Implicit Priors for Motion Optimization

Julen Urain, An T. Le, Alexander Lambert, Georgia Chalvatzaki, Byron Boots, Jan Peters

IROS 2022poster

Motion optimization is an effective framework for generating smooth and safe trajectories for robotic manipulation tasks. However, it suffers from local optima that hinder its applicability, especially for multi-objective tasks. In this paper, we study this problem in light of the integration of Ene…

Cited by 28 Source ↗Scholar ↗

2022

Stein Variational Probabilistic Roadmaps

Alexander Lambert, Brian Hou, Rosario Scalise, Siddhartha S. Srinivasa, Byron Boots

ICRA 2022poster

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 resolve the topology of a given environment, many sampling-based motion planners resort to coarse, heuristically-driven…

Cited by 9 Source ↗Scholar ↗

2021

Dual Online Stein Variational Inference for Control and Dynamics

Lucas Barcelos, Alexander Lambert, Rafael Oliveira, Paulo Borges, Byron Boots, Fabio Ramos

RSS 2021poster

Model predictive control (MPC) schemes have a proven track record for delivering aggressive and robust performance in many challenging control tasks; coping with nonlinear system dynamics; constraints; and observational noise. Despite their success; these methods often rely on simple control distrib…

Cited by 35 Source ↗PDF ↗code ↗Scholar ↗

2020

Stein Variational Model Predictive Control

Alexander Lambert, Fabio Ramos, Byron Boots, Dieter Fox, Adam Fishman

CoRL 2020

Decision making under uncertainty is critical to real-world, autonomous systems. Model Predictive Control (MPC) methods have demonstrated favorable performance in practice, but remain limited when dealing with complex probability distributions. In this paper, we propose a generalization of MPC that

Cited by 0 Source ↗PDF ↗Scholar ↗

2018

Deep Forward and Inverse Perceptual Models for Tracking and Prediction

Alexander Lambert, Amirreza Shaban, Amit Raj, Zhen Liu, Byron Boots

ICRA 2018poster

We consider the problems of learning forward models that map state to high-dimensional images and inverse models that map high-dimensional images to state in robotics. Specifically, we present a perceptual model for generating video frames from state with deep networks, and provide a framework for i…

Cited by 25 Source ↗Scholar ↗