Papers from Stanford AI Lab showcased at CoRL 2021 – Bridging Innovation and Human Appeal.
Introduction:
Welcome to the Conference on Robot Learning (CoRL 2021)! We’re thrilled to announce that SAIL will be presenting their groundbreaking work at this highly anticipated event. From language-informed actions to benchmarking household activities, the accepted papers cover a wide range of exciting topics in the field of robotics. The authors have provided links to their papers, videos, and blogs for your convenience. Don’t hesitate to reach out to the contact authors directly to learn more about the cutting-edge research happening at Stanford. With a diverse array of topics and contributions, CoRL 2021 promises to be an enlightening and engaging conference for all attendees. We can’t wait to see you there!
Full Article: Papers from Stanford AI Lab showcased at CoRL 2021 – Bridging Innovation and Human Appeal.
Exciting News: The Conference on Robot Learning (CoRL 2021) to be Held Next Week
CoRL 2021, the highly anticipated Conference on Robot Learning, is set to take place next week. This event promises to be an exciting opportunity to showcase the advancements in the field, particularly the work coming out of Stanford’s SAIL (Stanford Artificial Intelligence Lab). If you’re interested in learning more about the groundbreaking research being presented, we have provided links to papers, videos, and blogs below. Additionally, feel free to reach out to the contact authors directly for further information on the projects happening at Stanford.
List of Accepted Papers Showcasing Varied Topics
1. 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
2. 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
3. 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
4. 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
5. 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
6. GRAC: Self-Guided and Self-Regularized Actor-Critic
– Authors: Lin Shao, Yifan You, Mengyuan Yan, Shenli Yuan, Qingyun Sun, Jeannette Bohg
– Contact: harry473417@ucla.edu
– Links: Paper | Website
– Keywords: deep reinforcement learning, Q-learning
7. Influencing Towards Stable Multi-Agent Interactions
– Authors: Woodrow Z. Wang, Andy Shih, Annie Xie, Dorsa Sadigh
– Contact: woodywang153@gmail.com
– Award nominations: Oral presentation
– Links: Paper | Website
– Keywords: multi-agent interactions, human-robot interaction, non-stationarity
8. Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation
– Authors: Suraj Nair, Eric Mitchell, Kevin Chen, Brian Ichter, Silvio Savarese, Chelsea Finn
– Contact: surajn@stanford.edu
– Links: Paper | Website
– Keywords: natural language, offline RL, visuomotor manipulation
9. Learning Multimodal Rewards from Rankings
– Authors: Vivek Myers, Erdem Bıyık, Nima Anari, Dorsa Sadigh
– Contact: ebiyik@stanford.edu
– Links: Paper | Video | Website
– Keywords: reward learning, active learning, learning from rankings, multimodality
10. Learning Reward Functions from Scale Feedback
– Authors: Nils Wilde*, Erdem Bıyık*, Dorsa Sadigh, Stephen L. Smith
– Contact: ebiyik@stanford.edu
– Links: Paper | Video | Website
– Keywords: preference-based learning, reward learning, active learning, scale feedback
These are just a few examples of the diverse range of papers that will be presented at CoRL 2021. From learning to regrasp objects to the importance of learning from offline human demonstrations for robot manipulation, the conference covers a broad spectrum of topics that reflect the cutting-edge research in the field of robot learning.
Excitement Builds for CoRL 2021
The anticipation is building for CoRL 2021, where researchers, experts, and enthusiasts will come together to share knowledge, ideas, and insights. Whether you are interested in natural language processing, embodied AI, or reinforcement learning, CoRL 2021 promises to offer exciting opportunities for collaboration and learning.
Don’t miss this chance to be a part of the latest advancements and innovations in robot learning. Join us at CoRL 2021 and immerse yourself in the future of robotics. We look forward to seeing you there!
Summary: Papers from Stanford AI Lab showcased at CoRL 2021 – Bridging Innovation and Human Appeal.
The Conference on Robot Learning (CoRL 2021) is set to take place soon, showcasing the impressive work from SAIL. With a focus on natural language, shared autonomy, human-robot interaction, embodied AI, and benchmarking, the accepted papers cover a wide range of topics. Some highlights include learning diverse strategies for human-robot collaboration, differentiable rendering and identification of impact sounds, model-based reinforcement learning for long-horizon tasks, and learning multimodal rewards from rankings. Researchers and enthusiasts alike can expect an exciting and informative event.
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