# Event-Driven Proactive Assistive Manipulation with Grounded Vision-Language Planning

arXiv: https://arxiv.org/abs/2603.23950 · HTML: https://mlsanity.com/paper/2603.23950/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2026.

## Built on by (most cited first, 2 of 2)

- [Cognitive Action Reasoning for Proactive Robots from Human-Centered Multimodal Observations](https://mlsanity.com/paper/2609.23486/) (2026)
- [Proactive Physical AI through Continuous Observation and Timely Intervention](https://mlsanity.com/paper/2610.16967/) (2026)

## Abstract (arXiv)

Assistance in collaborative manipulation is often initiated by user instructions, making high-level reasoning request-driven. In fluent human teamwork, however, partners often infer the next helpful step from the observed outcome of an action rather than waiting for instructions. Motivated by this, we introduce a shift from request-driven assistance to event-driven proactive assistance, where robot actions are initiated by workspace state transitions induced by human--object interactions rather than user-provided task instructions. To this end, we propose an event-driven framework that tracks interaction progress with an event monitor and, upon event completion, extracts stabilized pre/post snapshots that characterize the resulting state transition. Given the stabilized snapshots, the planner analyzes the implied state transition to infer a task-level goal and decide whether to intervene; if so, it generates a sequence of assistive actions. To make outputs executable and verifiable, we restrict actions to a set of action primitives and reference objects via integer IDs. We evaluate the framework on a real tabletop number-block collaboration task, demonstrating that explicit pre/post state-change evidence improves proactive completion on solvable scenes and appropriate waiting on unsolvable ones.