robotics
A Phone Scan Became the Robot’s Training Ground
Splat2Act builds scene-specific practice from captured rooms and the user’s own objects.
Summary
Splat2Act builds scene-specific practice from captured rooms and the user’s own objects.
A generalist manipulation policy recorded zero pickup success in the authors’ captured deployment scenes before adaptation. Splat2Act uses a phone scan and object images to build a digital twin, discover reachable placement surfaces and generate successful simulated demonstrations. Across 101 campus environments, the pipeline produced more than 68,000 demonstrations. The strongest personalized policy reached 62.5% aggregate pickup success in its target scene. On a real Stretch 3 robot, it achieved 63.9% on a 12-task subset. The result supports scene-specific adaptation from simulated practice; it does not establish reliable manipulation in arbitrary homes.
Why it matters
Splat2Act builds scene-specific practice from captured rooms and the user’s own objects.
Limits and context
- The result supports scene-specific adaptation from simulated practice; it does not establish reliable manipulation in arbitrary homes.
Key claims
Splat2Act builds scene-specific practice from captured rooms and the user’s own objects.
Qualification: The result supports scene-specific adaptation from simulated practice; it does not establish reliable manipulation in arbitrary homes.
Evidence: source-2026-10-12-003
Sources
- arXiv preprint 2610.13945, original v1arXiv / submitting research authors · primary research
Corrections
: Publication note: The October 12, 2026 edition was prepared and published late on October 12 after the scheduled newsroom task could not start. The edition retains its October 12 date and 9:00 AM America/Detroit reveal time. Reports use sources available before that cutoff; actual preparation and image-generation times are preserved in the provenance records.