
Robotics has a data problem, but not the one you’re thinking of: almost all robotics data is of successful instances of robot behaviors. But many of the most exciting research areas for robotics today, including world models and reward model learning, require failures: interesting examples of manipulation trajectories that did not succeed.Claas Voelcker, Arpit Bahety, and Paul Zhou join us to talk about their data collection effort and their upcoming workshop on “oopsie data”: all these valuable failures that are currently being thrown out. Their goal is to collect the first large community-driven open-source dataset of failure examples.Watch Episode 114 of RoboPapers, with Chris Paxton and Jiafei Duan, to learn more!Learn MoreProject site: https://oopsie-data.com/ This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit robopapers.substack.com
Podzilla Summary coming soon
Sign up to get notified when the full AI-powered summary is ready.
Free forever for up to 3 podcasts. No credit card required.

Ep#115: Delta-0 (Δ₀): A New Chapter in Humanoid Intelligence

Ep#113: MolmoBOT: Large-Scale Simulation Enables Zero-Shot Manipulation

Ep#112: SlipSense and Touch2Trace

Ep#111: SOLE-R1: Video-Language Reasoning as the Sole Reward for On-Robot Reinforcement Learning
Free AI-powered recaps of RoboPapers and your other favorite podcasts, delivered to your inbox.
Free forever for up to 3 podcasts. No credit card required.