The Conference on Robot Learning (CoRL 2022) 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

PLATO: Predicting Latent Affordances Through Object-Centric Play

Authors: Suneel Belkhale, Dorsa Sadigh
Contact: dorsa@stanford.edu
Links: Paper | Video
Keywords: Human Play Data, Object Affordance Learning, Imitation Learning

A Dual Representation Framework for Robot Learning with Human Guidance

Authors: Ruohan Zhang, Dhruva Bansal, Yilun Hao, Ayano Hiranaka, Jialu Gao, Chen Wang, Roberto Martín-Martín, Li Fei-Fei, Jiajun Wu
Contact: zharu@stanford.edu
Links: Paper
Keywords: human guidance, evaluative feedback, preference learning

BEHAVIOR-1K: A Benchmark for Embodied AI with 1,000 Everyday Activities and Realistic Simulation

Authors: Chengshu Li, Ruohan Zhang, Josiah Wong, Cem Gokmen, Sanjana Srivastava, Roberto Martín-Martín, Chen Wang, Gabrael Levine, Michael Lingelbach, Jiankai Sun, Mona Anvari, Minjune Hwang, Manasi Sharma, Arman Aydin, Dhruva Bansal, Samuel Hunter, Kyu-Young Kim, Alan Lou, Caleb R Matthews, Ivan Villa-Renteria, Jerry Huayang Tang, Claire Tang, Fei Xia, Silvio Savarese, Hyowon Gweon, Karen Liu, Jiajun Wu, Li Fei-Fei
Contact: zharu@stanford.edu
Award nominations: Best paper nomination
Links: Paper | Website
Keywords: embodied ai benchmark, everyday activities, mobile manipulation

Eliciting Compatible Demonstrations for Multi-Human Imitation Learning

Authors: Kanishk Gandhi, Siddharth Karamcheti, Madeline Liao, Dorsa Sadigh
Contact: kanishk.gandhi@stanford.edu
Links: Paper | Website
Keywords: imitation learning, active learning, human-robot interaction

Few-Shot Preference Learning for Human-in-the-Loop Rl

Authors: Joey Hejna, Dorsa Sadigh
Contact: jhejna@stanford.edu
Links: Paper | Video | Website
Keywords: preference learning, interactive learning, multi-task learning, human-in-the-loop

Interpretable Self-Aware Neural Networks for Robust Trajectory Prediction

Authors: Masha Itkina, Mykel J. Kochenderfer
Contact: mitkina@stanford.edu
Links: Paper | Website
Keywords: autonomous vehicles, trajectory prediction, distribution shift

Learning Bimanual Scooping Policies for Food Acquisition

Authors: Jennifer Grannen*, Yilin Wu*, Suneel Belkhale, Dorsa Sadigh
Contact: jgrannen@stanford.edu
Links: Paper | Video | Website
Keywords: bimanual manipulation, food acquisition, robot-assisted feeding, deformable object manipulation

Learning Diverse and Physically Feasible Dexterous Grasps with Generative Model and Bilevel Optimization

Authors: Albert Wu, Michelle Guo, Karen Liu
Contact: amhwu@stanford.edu
Links: Paper | Video
Keywords: dexterous grasping, grasp planning, bilevel optimization, generative model

Learning Visuo-Haptic Skewering Strategies for Robot-Assisted Feeding

Authors: Priya Sundaresan, Suneel Belkhale, Dorsa Sadigh
Contact: priyasun@stanford.edu
Links: Paper | Video
Keywords: manipulation, deformable manipulation, perception, planning, computer vision

Leveraging Haptic Feedback to Improve Data Quality and Quantity for Deep Imitation Learning Models

Authors: Catie Cuan, Allison Okamura, Mohi Khansari
Contact: ccuan@stanford.edu
Links: Paper
Keywords: haptics and haptic interfaces, imitation learning, data curation

Offline Reinforcement Learning at Multiple Frequencies

Authors: Kaylee Burns ~Kaylee_Burns2 , Tianhe Yu, Chelsea Finn, Karol Hausman
Contact: kayburns@stanford.edu
Links: Paper | Video | Website
Keywords: offline reinforcement learning, robotics

R3M: A Universal Visual Representation for Robot Manipulation

Authors: Suraj Nair, Aravind Rajeswaran, Vikash Kumar, Chelsea Finn, Abhinav Gupta
Contact: surajn@stanford.edu
Links: Paper | Website
Keywords: visual representation learning, robotic manipulation

See, Hear, Feel: Smart Sensory Fusion for Robotic Manipulation

Authors: Hao Li*, Yizhi Zhang*, Junzhe Zhu, Shaoxiong Wang, Michelle A Lee, Huazhe Xu, Edward Adelson, Li Fei-Fei, Ruohan Gao†, Jiajun Wu†
Contact: rhgao@cs.stanford.edu
Links: Paper | Video | Website
Keywords: multisensory, robot learning, robotic manipulation

We look forward to seeing you at CoRL 2022!