# Embodied Tree of Thoughts: Deliberate Manipulation Planning with Embodied World Model arXiv: https://arxiv.org/abs/2512.08188 ยท HTML: https://mlsanity.com/paper/2512.08188/ ## Key facts - Cited by 1 papers in the map (data through 14 Oct 2026). - Placed in Embodied and robot world models, one of 11 directions in World models. - Builds on 33 of its 38 references that are in the map. - First posted on arXiv 9 Dec 2025. ## Placed in - World models / [Embodied and robot world models](https://mlsanity.com/wm/embodied-and-robot-world-models/) - LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/) ## Builds on (most cited first, 30 of 33) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023) - [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022) - [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023) - [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023) - [Inner Monologue: Embodied Reasoning through Planning with Language Models](https://mlsanity.com/paper/2207.05608/) (2022) - [SAM 3: Segment Anything with Concepts](https://mlsanity.com/paper/2511.16719/) (2025) - [FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects](https://mlsanity.com/paper/2312.08344/) (2023) - [ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation](https://mlsanity.com/paper/2409.01652/) (2024) - [One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusion](https://mlsanity.com/paper/2311.07885/) (2023) - [SAM 3D: 3Dfy Anything in Images](https://mlsanity.com/paper/2511.16624/) (2025) - [ManiSkill3: GPU Parallelized Robotics Simulation and Rendering for Generalizable Embodied AI](https://mlsanity.com/paper/2410.00425/) (2024) - [AnyGrasp: Robust and Efficient Grasp Perception in Spatial and Temporal Domains](https://mlsanity.com/paper/2212.08333/) (2022) - [REFLECT: Summarizing Robot Experiences for Failure Explanation and Correction](https://mlsanity.com/paper/2306.15724/) (2023) - [Look Before You Leap: Unveiling the Power of GPT-4V in Robotic Vision-Language Planning](https://mlsanity.com/paper/2311.17842/) (2023) - [BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with 1,000 Everyday Activities and Realistic Simulation](https://mlsanity.com/paper/2403.09227/) (2024) - [RoboBrain: A Unified Brain Model for Robotic Manipulation from Abstract to Concrete](https://mlsanity.com/paper/2502.21257/) (2025) - [ManiGaussian: Dynamic Gaussian Splatting for Multi-task Robotic Manipulation](https://mlsanity.com/paper/2403.08321/) (2024) - [CoPa: General Robotic Manipulation through Spatial Constraints of Parts with Foundation Models](https://mlsanity.com/paper/2403.08248/) (2024) - [Video Generators are Robot Policies](https://mlsanity.com/paper/2508.00795/) (2025) - [A Comprehensive Survey on World Models for Embodied AI](https://mlsanity.com/paper/2510.16732/) (2025) - [OmniManip: Towards General Robotic Manipulation via Object-Centric Interaction Primitives as Spatial Constraints](https://mlsanity.com/paper/2501.03841/) (2025) - [Tree-Planner: Efficient Close-loop Task Planning with Large Language Models](https://mlsanity.com/paper/2310.08582/) (2023) - [World-in-World: World Models in a Closed-Loop World](https://mlsanity.com/paper/2510.18135/) (2025) - [Reflective Planning: Vision-Language Models for Multi-Stage Long-Horizon Robotic Manipulation](https://mlsanity.com/paper/2502.16707/) (2025) - [3DFlowAction: Learning Cross-Embodiment Manipulation from 3D Flow World Model](https://mlsanity.com/paper/2506.06199/) (2025) - [VLMPC: Vision-Language Model Predictive Control for Robotic Manipulation](https://mlsanity.com/paper/2407.09829/) (2024) - [BUMBLE: Unifying Reasoning and Acting with Vision-Language Models for Building-wide Mobile Manipulation](https://mlsanity.com/paper/2410.06237/) (2024) - [Prompting with the Future: Open-World Model Predictive Control with Interactive Digital Twins](https://mlsanity.com/paper/2506.13761/) (2025) ## Built on by (most cited first, 1 of 1) - [PopPy: Opportunistically Exploiting Parallelism in Python Agentic Workflows](https://mlsanity.com/paper/2605.18697/) (2026) ## Abstract (arXiv) 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 .