Search Results for author:
Found 9 papers, 6 papers with code
Uncertainty-Aware Deployment of Pre-trained Language-Conditioned Imitation Learning Policies
Large-scale robotic policies trained on data from diverse tasks and robotic platforms hold great promise for enabling general-purpose robots; however, reliable generalization to new environment conditions remains a major challenge.
VLFM: Vision-Language Frontier Maps for Zero-Shot Semantic Navigation
Understanding how humans leverage semantic knowledge to navigate unfamiliar environments and decide where to explore next is pivotal for developing robots capable of human-like search behaviors.
EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy
For uncertainty quantification, we efficiently model both aleatoric and epistemic uncertainty by learning discrete traction distributions and probability densities of the traction predictor's latent features.
Uncertainty-driven Planner for Exploration and Navigation
We consider the problems of exploration and point-goal navigation in previously unseen environments, where the spatial complexity of indoor scenes and partial observability constitute these tasks challenging.
Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets
Robot learning holds the promise of learning policies that generalize broadly.
Learning to Map for Active Semantic Goal Navigation
We consider the problem of object goal navigation in unseen environments.
Learning Portrait Style Representations
We study style representations learned by neural network architectures incorporating these higher level characteristics.
An Adversarial Objective for Scalable Exploration
Model-based curiosity combines active learning approaches to optimal sampling with the information gain based incentives for exploration presented in the curiosity literature.
RoboNet: Large-Scale Multi-Robot Learning
This leads to a frequent tension in robotic learning: how can we learn generalizable robotic controllers without having to collect impractically large amounts of data for each separate experiment?
