Embodied Tree of Thoughts: Deliberate Manipulation Planning with Embodied World Model

• URL: http://arxiv.org/abs/2512.08188v1
• Date: Tue, 09 Dec 2025 02:36:26 GMT
• Title: Embodied Tree of Thoughts: Deliberate Manipulation Planning with Embodied World Model
• Authors: Wenjiang Xu, Cindy Wang, Rui Fang, Mingkang Zhang, Lusong Li, Jing Xu, Jiayuan Gu, Zecui Zeng, Rui Chen,
• Abstract summary: Embodied Tree of Thoughts (EToT) is a novel Real2Sim2Real planning framework.<n>EToT formulates manipulation planning as a tree search expanded through two synergistic mechanisms.<n>By grounding high-level reasoning in a physics simulator, our framework ensures that generated plans adhere to rigid-body dynamics and collision constraints.
• Score: 12.257547810949482
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: World models have emerged as a pivotal component in robot manipulation planning, enabling agents to predict future environmental states and reason about the consequences of actions before execution. While video-generation models are increasingly adopted, they often lack rigorous physical grounding, leading to hallucinations and a failure to maintain consistency in long-horizon physical constraints. To address these limitations, we propose Embodied Tree of Thoughts (EToT), a novel Real2Sim2Real planning framework that leverages a physics-based interactive digital twin as an embodied world model. EToT formulates manipulation planning as a tree search expanded through two synergistic mechanisms: (1) Priori Branching, which generates diverse candidate execution paths based on semantic and spatial analysis; and (2) Reflective Branching, which utilizes VLMs to diagnose execution failures within the simulator and iteratively refine the planning tree with corrective actions. By grounding high-level reasoning in a physics simulator, our framework ensures that generated plans adhere to rigid-body dynamics and collision constraints. We validate EToT on a suite of short- and long-horizon manipulation tasks, where it consistently outperforms baselines by effectively predicting physical dynamics and adapting to potential failures. Website at https://embodied-tree-of-thoughts.github.io .

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