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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.23950 |
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| _version_ | 1866918407648051200 |
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| author | Liu, Fengkai Su, Hao Chi, Haozhuang Geng, Rui Ren, Congzhi Liu, Xuqing Xu, Yucheng Ohsita, Yuichi Zhang, Liyun |
| author_facet | Liu, Fengkai Su, Hao Chi, Haozhuang Geng, Rui Ren, Congzhi Liu, Xuqing Xu, Yucheng Ohsita, Yuichi Zhang, Liyun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_23950 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Event-Driven Proactive Assistive Manipulation with Grounded Vision-Language Planning Liu, Fengkai Su, Hao Chi, Haozhuang Geng, Rui Ren, Congzhi Liu, Xuqing Xu, Yucheng Ohsita, Yuichi Zhang, Liyun Robotics 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. |
| title | Event-Driven Proactive Assistive Manipulation with Grounded Vision-Language Planning |
| topic | Robotics |
| url | https://arxiv.org/abs/2603.23950 |