Waterloo Robot Learning Reading Group - Data Collection Methods (Week 5)
Meetup to discuss state-of-the-art research on robot learning (humanoids, foundation models, RL, sim2real, etc.), similar to the Toronto ML/Systems Reading Group and Vector Institute's Machine Learning Lunches- list of topics & articles below - all are welcome! 🎉
Pizza sponsored by Palatial!
Previous week: https://luma.com/ympgqji6
Topic - Data Collection Methods
6:00pm - Xu, et al., 2025. DexUMI: Using Human Hand as the Universal Manipulation Interface for Dexterous Manipulation. Presented by Steven Gong
Additional Reading List:
Grauman, et al. 2022. Ego4D: Around the World in 3,000 Hours of Egocentric Video.
Wu, et al., 2023. GELLO: A General, Low-Cost, and Intuitive Teleoperation Framework for Robot Manipulators.
Zhao, et al. 2023. Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware.
Chi, et al., 2024. Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots.
Etukuru, et. al. 2024. Robot Utility Models: General Policies for Zero-Shot Deployment in New Environments.
Iyer, et al. 2024. OPEN TEACH: A Versatile Teleoperation System for Robotic Manipulation.
Si, et al., 2025. ExoStart: Efficient learning for dexterous manipulation with sensorized exoskeleton demonstrations.
Wu, et al., 2025. MagiClaw: A Dual-Use, Vision-Based Soft Gripper for Bridging the Human Demonstration to Robotic Deployment Gap.
Workshops:
Robot Learning: Methods and Considerations for Scaling Data Collection by MILA
The 1st Workshop on Making Sense of Data in Robotics at CoRL 2025
Questions we should answer as we read these papers:
What are the authors trying to do? Articulate their objectives.
How was it done prior to their work, and what were the limits of current practice?
What is new in their approach, and why do they think it will be successful?
What are the mid-term and final “exams” to check for success? (i.e., How is the method evaluated?)
What are limitations that the author mentions (any that they omit)?