Latest papers: Difference between revisions
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This page serves to collect notable research papers within the past two years related to [[robotics]] and [[artificial intelligence]]. Feel free to add new papers to the list and discuss any papers on the [[Talk:Latest_papers|talk page]]. | This page serves to collect notable research papers within the past two years related to [[robotics]] and [[artificial intelligence]]. Feel free to add new papers to the list and discuss any papers on the [[Talk:Latest_papers|talk page]]. | ||
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==== [[Computer vision]] ==== | ==== [[Computer vision]] ==== | ||
{{paper|title=NeuralMVS: Bridging Multi-View Stereo and Novel View Synthesis|url=https://arxiv.org/abs/2108.03880|tldr=[[Multi-view stereo]] is a core task in 3D computer vision. [[Neural Radiance Fields|NeRF]] methods do not generalize to novel scenes and are slow to train and test. We propose to bridge the gap between these two methodologies with a novel network that can recover 3D scene geometry as a distance function.|authors=Radu Alexandru Rosu, Sven Behnke|publication=arXiv:2108.03880|year=2021}} | {{paper|title=NeuralMVS: Bridging Multi-View Stereo and Novel View Synthesis|url=https://arxiv.org/abs/2108.03880|tldr=[[Multi-view stereo]] is a core task in 3D computer vision. [[Neural Radiance Fields|NeRF]] methods do not generalize to novel scenes and are slow to train and test. We propose to bridge the gap between these two methodologies with a novel network that can recover 3D scene geometry as a distance function.|authors=Radu Alexandru Rosu, Sven Behnke|publication=arXiv:2108.03880|year=2021}} | ||
==== [[Simulation]] ==== | |||
{{paper|title=iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks|url=http://svl.stanford.edu/igibson/|tldr=iGibson 2.0 is a novel simulation environment using [[Bullet]] that supports the simulation of a more diverse set of household tasks through three key innovations. Firstly, it supports object states, including temperature, wetness level, cleanliness level, and toggled and sliced states, necessary to cover a wider range of tasks. Second, it implements a set of predicate logic functions that map the simulator states to logic states like Cooked or Soaked. Third, the simulator can sample valid physical states that satisfy a logic state. This functionality can generate potentially infinite instances of tasks with minimal effort from the users.|publication=arXiv:2108.03272|authors=Chengshu Li, Fei Xia, Roberto Martín-Martín, Michael Lingelbach, Sanjana Srivastava, Bokui Shen, Kent Vainio, Cem Gokmen, Gokul Dharan, Tanish Jain, Andrey Kurenkov, Karen Liu, Hyowon Gweon, Jiajun Wu, Li Fei-Fei, Silvio Savarese|year=2021}} | |||
=== June 2021 === | === June 2021 === | ||
Revision as of 14:22, 10 August 2021
{{expand| This page serves to collect notable research papers within the past two years related to robotics and artificial intelligence. Feel free to add new papers to the list and discuss any papers on the talk page.
Recent papers
August 2021
Computer vision
NeuralMVS: Bridging Multi-View Stereo and Novel View Synthesis (arXiv:2108.03880)
tl;dr Multi-view stereo is a core task in 3D computer vision. NeRF methods do not generalize to novel scenes and are slow to train and test. We propose to bridge the gap between these two methodologies with a novel network that can recover 3D scene geometry as a distance function.[1]
Simulation
iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks (arXiv:2108.03272)
tl;dr iGibson 2.0 is a novel simulation environment using Bullet that supports the simulation of a more diverse set of household tasks through three key innovations. Firstly, it supports object states, including temperature, wetness level, cleanliness level, and toggled and sliced states, necessary to cover a wider range of tasks. Second, it implements a set of predicate logic functions that map the simulator states to logic states like Cooked or Soaked. Third, the simulator can sample valid physical states that satisfy a logic state. This functionality can generate potentially infinite instances of tasks with minimal effort from the users.[2]
June 2021
Multimodal learning
Multimodal Few-Shot Learning with Frozen Language Models (arXiv:2106.13884)
tl;dr When trained at sufficient scale, auto-regressive language models exhibit the notable ability to learn a new language task after being prompted with just a few examples. Here, the authors present a simple, yet effective, approach for transferring this few-shot learning ability to a multimodal setting (vision and language).[3]
Optimizers
A Generalizable Approach to Learning Optimizers (arXiv:2106.00958)
tl;dr Learning to update optimizer hyperparameters instead of model parameters directly using novel features, actions, and a reward function.[4]
October 2020
Computer vision
GRF: Learning a General Radiance Field for 3D Scene Representation and Rendering (arXiv:2010.04595)
tl;dr General Radiance Fields construct an internal representation for each 3D point of a scene from 2D inputs and renders the corresponding appearance and geometry of any 3D scene viewing from an arbitrary angle.[5]
September 2020
Summarization
Learning to Summarize with Human Feedback (arXiv:2009.01325)
tl;dr Human feedback models outperform much larger supervised models and reference summaries on TL;DR.[6]
Older papers
References
- ↑ Radu Alexandru Rosu, Sven Behnke. NeuralMVS: Bridging Multi-View Stereo and Novel View Synthesis. arXiv:2108.03880, 2021.
- ↑ Chengshu Li, Fei Xia, Roberto Martín-Martín, Michael Lingelbach, Sanjana Srivastava, Bokui Shen, Kent Vainio, Cem Gokmen, Gokul Dharan, Tanish Jain, Andrey Kurenkov, Karen Liu, Hyowon Gweon, Jiajun Wu, Li Fei-Fei, Silvio Savarese. iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks. arXiv:2108.03272, 2021.
- ↑ Maria Tsimpoukelli, Jacob Menick, Serkan Cabi, S. M. Ali Eslami, Oriol Vinyals, Felix Hill. Multimodal Few-Shot Learning with Frozen Language Models. arXiv:2106.13884, 2021.
- ↑ Diogo Almeida, Clemens Winter, Jie Tang, Wojciech Zaremba. A Generalizable Approach to Learning Optimizers. arXiv:2106.00958, 2021.
- ↑ Alex Trevithick, Bo Yang. GRF: Learning a General Radiance Field for 3D Scene Representation and Rendering. arXiv:2010.04595, 2020.
- ↑ Nisan Stiennon, Long Ouyang, Jeff Wu, Daniel M. Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, Paul Christiano. Learning to Summarize with Human Feedback. arXiv:2009.01325, 2020.