Search Results for author: Daniel Ho

Found 11 papers, 3 papers with code

Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

no code implementations4 Apr 2022 Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Daniel Ho, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Eric Jang, Rosario Jauregui Ruano, Kyle Jeffrey, Sally Jesmonth, Nikhil J Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Kuang-Huei Lee, Sergey Levine, Yao Lu, Linda Luu, Carolina Parada, Peter Pastor, Jornell Quiambao, Kanishka Rao, Jarek Rettinghouse, Diego Reyes, Pierre Sermanet, Nicolas Sievers, Clayton Tan, Alexander Toshev, Vincent Vanhoucke, Fei Xia, Ted Xiao, Peng Xu, Sichun Xu, Mengyuan Yan

We show how low-level skills can be combined with large language models so that the language model provides high-level knowledge about the procedures for performing complex and temporally-extended instructions, while value functions associated with these skills provide the grounding necessary to connect this knowledge to a particular physical environment.

Decision Making Language Modelling

Bayesian Imitation Learning for End-to-End Mobile Manipulation

no code implementations15 Feb 2022 Yuqing Du, Daniel Ho, Alexander A. Alemi, Eric Jang, Mohi Khansari

In this work we investigate and demonstrate benefits of a Bayesian approach to imitation learning from multiple sensor inputs, as applied to the task of opening office doors with a mobile manipulator.

Imitation Learning

Practical Imitation Learning in the Real World via Task Consistency Loss

no code implementations3 Feb 2022 Mohi Khansari, Daniel Ho, Yuqing Du, Armando Fuentes, Matthew Bennice, Nicolas Sievers, Sean Kirmani, Yunfei Bai, Eric Jang

To the best of our knowledge, this is the first work to tackle latched door opening from a purely end-to-end learning approach, where the task of navigation and manipulation are jointly modeled by a single neural network.

Domain Adaptation Imitation Learning

Universal Controllers with Differentiable Physics for Online System Identification

no code implementations29 Sep 2021 Michelle Guo, Wenhao Yu, Daniel Ho, Jiajun Wu, Yunfei Bai, Karen Liu, Wenlong Lu

In addition, we perform two studies showing that UC-DiffOSI operates well in environments with changing or unknown dynamics.

Domain Adaptation

NBDT: Neural-Backed Decision Tree

no code implementations ICLR 2021 Alvin Wan, Lisa Dunlap, Daniel Ho, Jihan Yin, Scott Lee, Suzanne Petryk, Sarah Adel Bargal, Joseph E. Gonzalez

Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use.

COCOI: Contact-aware Online Context Inference for Generalizable Non-planar Pushing

no code implementations23 Nov 2020 Zhuo Xu, Wenhao Yu, Alexander Herzog, Wenlong Lu, Chuyuan Fu, Masayoshi Tomizuka, Yunfei Bai, C. Karen Liu, Daniel Ho

General contact-rich manipulation problems are long-standing challenges in robotics due to the difficulty of understanding complicated contact physics.

RICA: Evaluating Robust Inference Capabilities Based on Commonsense Axioms

no code implementations EMNLP 2021 Pei Zhou, Rahul Khanna, Seyeon Lee, Bill Yuchen Lin, Daniel Ho, Jay Pujara, Xiang Ren

Pre-trained language models (PTLMs) have achieved impressive performance on commonsense inference benchmarks, but their ability to employ commonsense to make robust inferences, which is crucial for effective communications with humans, is debated.

NBDT: Neural-Backed Decision Trees

2 code implementations1 Apr 2020 Alvin Wan, Lisa Dunlap, Daniel Ho, Jihan Yin, Scott Lee, Henry Jin, Suzanne Petryk, Sarah Adel Bargal, Joseph E. Gonzalez

Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use.

Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules

3 code implementations14 May 2019 Daniel Ho, Eric Liang, Ion Stoica, Pieter Abbeel, Xi Chen

A key challenge in leveraging data augmentation for neural network training is choosing an effective augmentation policy from a large search space of candidate operations.

Image Augmentation

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