# imitation Published articles for imitation. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Jack of All Trades, Master of Some, a Multi-Purpose Transformer Agent DevFeed: [Jack of All Trades, Master of Some, a Multi-Purpose Transformer Agent]() Original publisher: [Read original article]() Author: Quentin Gallouédec; Edward Beeching; Clément ROMAC; Thomas Wolf Published: 2024-04-22T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Transformer](), [Artificial Intelligence](), [datasets](), [Open Source](), [Machine Learning & Artificial Intelligence](), [Embeddings]() Tags: [agent](), [architecture](), [datasets](), [embeddings](), [games](), [generalist](), [imitation](), [model](), [open-source](), [rl](), [train](), [transformers]() ### AI overview The article introduces Jack of All Trades (JAT), an open-source project for training a generalist multimodal Transformer agent. It describes the JAT dataset, expert reinforcement-learning agents, and a model that can handle tasks including video games, robot control, command execution, and navigation. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Stanford AI Lab Papers and Talks at NeurIPS 2021 DevFeed: [Stanford AI Lab Papers and Talks at NeurIPS 2021]() Original publisher: [Read original article]() Author: Compiled by Drew A. Hudson Published: 2021-12-06T08:00:00Z Content type: article Language: en Sources: [The Stanford AI Lab Blog]() Topics: [NeurIPS](), [Artificial Intelligence](), [Reverse Engineering](), [data](), [Reinforcement learning]() Tags: [ai](), [generative](), [imitation](), [learning](), [models](), [neural-networks](), [neurips](), [reinforcement-learning](), [reverse-engineering](), [transformers]() ### AI overview Stanford AI Lab presents its research at NeurIPS 2021, including work on generative models, recurrent and transformer-based architectures, state-space models, emergent communication, reinforcement learning, imitation learning, and neural coding. ### Source excerpt The thirty-fifth Conference on Neural Information Processing Systems (NeurIPS) 2021 is being hosted virtually from Dec 6th - 14th. We're excited to share all the work from SAIL that's being presented at the main conference, at the Datasets and Benchmarks track and the various workshops, and you'll find links to papers, videos and blogs below. Some of the members in our SAIL community also serve as co-organizers of several exciting workshops that will take place on Dec 13-14, so we hope you will check them out! Feel free to reach out to the contact authors and the workshop organizers directly to learn more about the work that's happening at Stanford! Main Conference Improving Compositionality of Neural Networks by Decoding Representations to Inputs Authors: Mike Wu, Noah Goodman, Stefano Ermon Contact: wumike@stanford.edu Links: Paper Keywords: generative models, compositionality, decoder Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems Authors: Jimmy T.H. Smith, Scott W. Linderman, David Sussillo Contact: jsmith14@stanford.edu Links: Paper | Website Keywords: recurrent neural networks, switching linear dynamical systems, interpretability, fixed points Compositional Transformers for Scene Generation Authors: Drew A. Hudson, C. Lawrence Zitnick Contact: dorarad@cs.stanford.edu Links: Paper | Github Keywords: GANs, transformers, compositionality, scene synthesis Combining Recurrent, Convolutional, and Continuous-time Models with Linear State Space Layers Authors: Albert Gu, Isys Johnson, Karan Goel, Khaled Saab, Tri Dao, Atri Rudra, Chris Ré Contact: albertgu@stanford.edu Links: Paper Keywords: recurrent neural networks, rnn, continuous models, state space, long range dependencies, sequence modeling Emergent Communication of Generalizations Authors: Jesse Mu, Noah Goodman Contact: muj@stanford.edu Links: Paper | Video Keywords: emergent communication, multi-agent communication, language grounding, compositionality Deep Lear ## Stanford AI Lab Papers at CoRL 2021 DevFeed: [Stanford AI Lab Papers at CoRL 2021]() Original publisher: [Read original article]() Author: Compiled by Drew A. Hudson Published: 2021-11-05T07:00:00Z Content type: article Language: en Sources: [The Stanford AI Lab Blog]() Topics: [Reinforcement learning](), [Artificial Intelligence](), [benchmarking](), [Robotic manipulation](), [Benchmark]() Tags: [ai](), [benchmark](), [benchmarking](), [imitation](), [learning](), [offline](), [reinforcement-learning](), [rl](), [robotic-manipulation]() ### AI overview The Stanford AI Lab presents its work accepted at the Conference on Robot Learning (CoRL 2021). The article lists projects covering language-informed robot autonomy, household-activity benchmarking, human-robot collaboration, imitation learning, model-based and deep reinforcement learning, reward and preference learning, and robotic manipulation. ### Source excerpt The Conference on Robot Learning (CoRL 2021) will take place next week. We're excited to share all the work from SAIL that will be presented, and you'll find links to papers, videos and blogs below. Feel free to reach out to the contact authors directly to learn more about the work that's happening at Stanford! List of Accepted Papers LILA: Language-Informed Latent Actions Authors: Siddharth Karamcheti*, Megha Srivastava*, Percy Liang, Dorsa Sadigh Contact: skaramcheti@cs.stanford.edu, megha@cs.stanford.edu Keywords: natural language, shared autonomy, human-robot interaction BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environments Authors: Sanjana Srivastava*, Chengshu Li*, Michael Lingelbach*, Roberto Martín-Martín*, Fei Xia, Kent Vainio, Zheng Lian, Cem Gokmen, Shyamal Buch, C. Karen Liu, Silvio Savarese, Hyowon Gweon, Jiajun Wu, Li Fei-Fei Contact: sanjana2@stanford.edu Links: Paper | Website Keywords: embodied ai, benchmarking, household activities Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration Authors: Chen Wang, Claudia Pérez-D'Arpino, Danfei Xu, Li Fei-Fei, C. Karen Liu, Silvio Savarese Contact: chenwj@stanford.edu Links: Paper | Website Keywords: learning for human-robot collaboration, imitation learning DiffImpact: Differentiable Rendering and Identification of Impact Sounds Authors: Samuel Clarke, Negin Heravi, Mark Rau, Ruohan Gao, Jiajun Wu, Doug James, Jeannette Bohg Contact: spclarke@stanford.edu Links: Paper | Website Keywords: differentiable sound rendering, auditory scene analysis Example-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks Authors: Bohan Wu, Suraj Nair, Li Fei-Fei*, Chelsea Finn* Contact: bohanwu@cs.stanford.edu Links: Paper Keywords: model-based reinforcement learning, long-horizon tasks GRAC: Self-Guided and Self-Regularized Actor-Critic Authors: Lin Shao, Yifan You, Mengyuan Yan, Shenli Yuan, Qingyun Sun, Jeannette Bohg Contact: