Search Results for author: Bernadette Bucher

Found 11 papers, 8 papers with code

NL-SLAM for OC-VLN: Natural Language Grounded SLAM for Object-Centric VLN

no code implementations12 Nov 2024 Sonia Raychaudhuri, Duy Ta, Katrina Ashton, Angel X. Chang, Jiuguang Wang, Bernadette Bucher

We present a new dataset, OC-VLN, in order to distinctly evaluate grounding object-centric natural language navigation instructions in a method for performing landmark-based navigation.

Instruction Following Object +1

Continuously Improving Mobile Manipulation with Autonomous Real-World RL

no code implementations30 Sep 2024 Russell Mendonca, Emmanuel Panov, Bernadette Bucher, Jiuguang Wang, Deepak Pathak

We present a fully autonomous real-world RL framework for mobile manipulation that can learn policies without extensive instrumentation or human supervision.

Uncertainty-Aware Deployment of Pre-trained Language-Conditioned Imitation Learning Policies

1 code implementation27 Mar 2024 Bo Wu, Bruce D. Lee, Kostas Daniilidis, Bernadette Bucher, Nikolai Matni

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.

Imitation Learning

VLFM: Vision-Language Frontier Maps for Zero-Shot Semantic Navigation

1 code implementation6 Dec 2023 Naoki Yokoyama, Sehoon Ha, Dhruv Batra, Jiuguang Wang, Bernadette Bucher

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.

Language Modelling Navigate

EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy

2 code implementations10 Nov 2023 Xiaoyi Cai, Siddharth Ancha, Lakshay Sharma, Philip R. Osteen, Bernadette Bucher, Stephen Phillips, Jiuguang Wang, Michael Everett, Nicholas Roy, Jonathan P. How

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 Quantification

Uncertainty-driven Planner for Exploration and Navigation

1 code implementation24 Feb 2022 Georgios Georgakis, Bernadette Bucher, Anton Arapin, Karl Schmeckpeper, Nikolai Matni, Kostas Daniilidis

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.

Learning Portrait Style Representations

1 code implementation8 Dec 2020 Sadat Shaik, Bernadette Bucher, Nephele Agrafiotis, Stephen Phillips, Kostas Daniilidis, William Schmenner

We study style representations learned by neural network architectures incorporating these higher level characteristics.

Image Generation Zero-Shot Learning

An Adversarial Objective for Scalable Exploration

1 code implementation13 Mar 2020 Bernadette Bucher, Karl Schmeckpeper, Nikolai Matni, Kostas Daniilidis

Model-based curiosity combines active learning approaches to optimal sampling with the information gain based incentives for exploration presented in the curiosity literature.

Active Learning

RoboNet: Large-Scale Multi-Robot Learning

no code implementations24 Oct 2019 Sudeep Dasari, Frederik Ebert, Stephen Tian, Suraj Nair, Bernadette Bucher, Karl Schmeckpeper, Siddharth Singh, Sergey Levine, Chelsea Finn

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?

Video Prediction

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