robotics
The Pouring Robot Estimates What the Camera Cannot See
FlowVLA tracks transferred mass from visual, joint, wrist-force and recurrent histories, then predicts residual outflow.
Summary
FlowVLA tracks transferred mass from visual, joint, wrist-force and recurrent histories, then predicts residual outflow.
In coffee-pot and water tests, the controller reported 13.1 grams mean absolute error across goals; 53.7 percent of attempts both completed and landed within ten percent of the requested mass. The same model interpolated to held-out 150- and 250-gram targets. Results are task-specific and still leave substantial error.
Why it matters
FlowVLA tracks transferred mass from visual, joint, wrist-force and recurrent histories, then predicts residual outflow.
Limits and context
No additional limitation was separately recorded.
Key claims
FlowVLA tracks transferred mass from visual, joint, wrist-force and recurrent histories, then predicts residual outflow.
Evidence: source-2026-10-22-021
Sources
- arXiv preprint 2610.29692, original v1arXiv · primary research
Corrections
No corrections have been recorded for this story.