ABM++: Learning Generalizable Manipulation Policies with a Mask-Guided World Model
Bibliographic record
Abstract
Achieving robust generalization across diverse scenarios is crucial for advancing the practical application of robotics. Existing approaches typically rely on interaction object masks as visual inputs to predict subsequent actions, gaining a certain degree of generalization capability. However, these methods primarily map visual inputs and task-relevant object masks to expert actions, overlooking the environmental dynamics that govern physical interactions among objects during manipulation. To overcome these limitations, we introduce ABM++, a novel framework that leverages pre-trained VLMs to build a mask-guided world model (MGWM) within an imitation learning paradigm for generalized robotic manipulation. Specifically, we extend a world model into a coarse-to-fine imitation learning framework to reconstruct future mask-dominated visual features, which enables the model to capture state transitions between the current and next states based on predicted actions, effectively modeling environmental dynamics. Comprehensive experiments demonstrate that ABM++ significantly surpasses established baselines in both simulation and real-world environments, achieving a relative improvement of 12.2% across 8 complex tasks, which underscores the superiority of our method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".