Plan Along the Way: Event-Triggered Foundation-Model Planning for TAMP Execution in Partially Observable Manipulation

[2608.28075] Deciding If, When, and Where to Replan for Robust Manipulation Skip to main content Search Submit Donate Log in Computer Science > Robotics arXiv:2608.28075 (cs) [Submitted on 28 Aug 2026 ( v1 ), last revised 10 Oct 2026 (this version, v2)] Title: Deciding If, When, and Where to Replan for Robust Manipulation Authors: Narendhiran Vijayakumar , Puru Ojha , Nav Singhal , Girish Varma , Antony Thomas View PDF HTML (experimental) Abstract: Foundation-model (FM) based task and motion planning (TAMP) methods typically assume that the full scene is known before planning. In long-horizon manipulation, however, relevant objects may initially be hidden and only become observable as the robot interacts with the environment. We introduce Robust-TAMP, a task and motion planning framework that first decides whether a discovery needs a replan. If replanning is needed, the robot executes unaffected actions while the FM synthesizes corrective actions. These actions are inserted into the existing plan based on urgency, either executed immediately or deferred while preserving the remaining plan. We evaluate Robust-TAMP on 14 MuJoCo environments with hidden objects, blocked placements, articulated containers, and multi-step tasks, and on five real-world settings. Our proposed method completes 92.1% of simulated tasks, outperforming four baseline comparison architectures by 47.1 percentage points under the same settings. Ablations show that each component either raises success or lowers planner calls, planning time, and robot idle time. An extensive study across model sizes, text and vision inputs, reasoning modes, and prompting further shows that in-context examples help the planner follow multi-step procedures that zero-shot prompting struggles with. Our implementation code and project details are available at this https URL . Comments: Submitted to AAMAS'27 Subjects: Robotics (cs.RO) Cite as: arXiv:2608.28075 [cs.RO] (or arXiv:2608.28075v2 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2608.28075 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Narendhiran Vijayakumar [ view email ] [v1] Fri, 28 Aug 2026 08:44:47 UTC (818 KB) [v2] Sat, 10 Oct 2026 21:03:10 UTC (18,241 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.RO < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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