HeLM: Hierarchical Explicit Language Memory for Non-Markovian Manipulation
Overview
This research presents HeLM, a hierarchical Vision-Language-Action framework designed to handle non-Markovian manipulation tasks by maintaining task-relevant history through explicit language memory. Evaluated across 10 real-world tasks with 56 variants, including five newly formulated inter-episode tasks, HeLM improves intra-episode success by 12.8 percentage points over the strongest baseline and achieves 78% subtask success on compositional generalization tasks. The framework uses up to 4.5x less memory and runs 2.5x faster than the keyframe baseline at long horizons.
Generated from attributed reports · Updated 2 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · memory reduction factor: 4.5 other · Basis not reported
“M improves intra-episode success by 12.8 percentage points over the strongest baseline and achieves 78% subtask success on compositional generalization tasks, while using up to 4.5x less memory and running 2.5x faster than the keyframe baseline at long horizons.”
Exact source · revision 1Source owner not reported
Reported quantity · speedup factor: 2.5 other · Basis not reported
“M improves intra-episode success by 12.8 percentage points over the strongest baseline and achieves 78% subtask success on compositional generalization tasks, while using up to 4.5x less memory and running 2.5x faster than the keyframe baseline at long horizons.”
Exact source · revision 1Source owner not reported
Reported quantity · number of task variants evaluated: 56 other · Basis not reported
“inner monologue', we propose Hierarchical Explicit Language Memory (HeLM), a hierarchical VLA framework that maintains task-relevant history in explicit language through factorized event detection and event-triggered memory updates. We evaluate HeLM across 10 non-Markovian real-world tasks with 56 variants, including five newly formulated inter-episode tasks that require persistent context across task boundaries. HeL”
Exact source · revision 1Source owner not reported
Report timeline
Follow attributed reports and material updates.
- arXiv Robotics — research abstractsSignalHeLM: Hierarchical Explicit Language Memory for Non-Markovian Manipulation
This research presents HeLM, a hierarchical Vision-Language-Action framework designed to handle non-Markovian manipulation tasks by maintaining task-relevant history through explicit language memory. Evaluated across 10 real-world tasks with 56 variants, including five newly formulated inter-episode tasks, HeLM improves intra-episode success by 12.8 percentage points over the strongest baseline and achieves 78% subtask success on compositional generalization tasks. The framework uses up to 4.5x less memory and runs 2.5x faster than the keyframe baseline at long horizons.
Event coverage history
There is not enough continuous observation data to show a trend.
Timezone · UTC
Article dates follow your selected timezone. Briefing editions use Hong Kong time (UTC+8).