robotics
Thirty Minutes of Practice Corrects a Mobile Manipulator
HiRe-MoMa freezes a base policy, learns separate residual corrections for the mobile base and arm, and uses sparse human interventions to shape exploration.
Summary
HiRe-MoMa freezes a base policy, learns separate residual corrections for the mobile base and arm, and uses sparse human interventions to shape exploration.
Fine-tuning a mobile manipulator on hardware entangles two different control problems: moving the base and handling objects with the arm. HiRe-MoMa leaves the original policy frozen and learns additive corrections through separate, one-way-conditioned heads. Human interventions covering less than 20 percent of training reshape the value estimate rather than becoming a full demonstration set. The authors report consistent gains on a TIAGo++ robot after roughly 30 minutes of online interaction across different base policies. That is evidence from the evaluated tasks and platform, not a general deployment guarantee.
Why it matters
HiRe-MoMa freezes a base policy, learns separate residual corrections for the mobile base and arm, and uses sparse human interventions to shape exploration.
Limits and context
- That is evidence from the evaluated tasks and platform, not a general deployment guarantee.
Key claims
HiRe-MoMa freezes a base policy, learns separate residual corrections for the mobile base and arm, and uses sparse human interventions to shape exploration.
Qualification: That is evidence from the evaluated tasks and platform, not a general deployment guarantee.
Evidence: source-2026-10-15-003
Sources
- arXiv preprint 2610.20607, original v1arXiv / submitting research authors · primary research
Corrections
: Publication note: This October 15 edition was prepared before its scheduled 9:00 AM America/Detroit reveal, but publication was delayed by a production build failure. The original reporting and image-generation timestamps are preserved.