robotics
Long Robot Tasks Get Atomic Credit
AtomicRL prevents failures in one subtask from contaminating credit across the whole manipulation sequence.
Summary
AtomicRL prevents failures in one subtask from contaminating credit across the whole manipulation sequence.
Long-horizon robot policies often receive one delayed reward, obscuring which subtask failed. AtomicRL gates temporal-difference residuals at semantic task boundaries and targets exploration at the least-mastered atomic step. On LIBERO-LONG, the paper reports 3.6- and 4.0-point gains over PPO across two VLA architectures; on CALVIN ABC-D, full-task performance improved by as much as 9.5 points. A Real2Sim2Real pipeline raised average real-world task performance by 21.3 percent in the reported tests.
Why it matters
AtomicRL prevents failures in one subtask from contaminating credit across the whole manipulation sequence.
Limits and context
No additional limitation was separately recorded.
Key claims
AtomicRL prevents failures in one subtask from contaminating credit across the whole manipulation sequence.
Evidence: source-2026-10-22-009
Sources
- arXiv preprint 2610.30097, original v1arXiv · primary research
Corrections
No corrections have been recorded for this story.