Michael Beetz

Generated robot futures can actively harm performance if temporally misaligned, but a new control-based conditioning method recovers substantial success by learning to trust and align them.

Frozen VLAs can recover from unseen actuator faults by inferring joint capabilities online, boosting success rates from 24.8% to 59.3% on 28 tasks.

Causal diagnosis transforms robot action testing from a blind resampling process into a targeted, efficient strategy that reduces failures by over a third.

LLM-guided, short-horizon future videos can significantly boost robot manipulation policy learning, even when the predicted future isn't perfect.