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SpatialCoT: Advancing Spatial Reasoning through Coordinate Alignment and Chain-of-Thought for Embodied Task Planning
Yuecheng Liu, Dafeng Chi, Shiguang Wu, Zhanguang Zhang, Yaochen Hu, Lingfeng Zhang, Yingxue Zhang, Shuang Wu, Tongtong Cao, Guowei Huang, Helong Huang, Guangjian Tian, Weichao Qiu, Xingyue Quan, Jianye Hao, Yuzheng Zhuang
TL;DR
Existing spatial-reasoning methods struggle with complex embodied tasks because language-only outputs and direct point prediction do not fully use VLM reasoning. SpatialCoT combines bidirectional coordinate alignment with chain-of-thought spatial grounding, and it outperforms prior methods on navigation and manipulation tasks, while remaining limited for rotations and dependent on 2D inputs.
Problem
Existing methods provide limited support for complex embodied tasks because language-based outputs are coarse and point-based methods neglect VLM language-based reasoning.
Method
SpatialCoT trains VLMs in two stages: spatial coordinate bi-directional alignment and chain-of-thought spatial grounding.
Results
SpatialCoT outperforms previous methods on challenging embodied tasks, including navigation and manipulation.
Takeaways & Limitations
Anchoring language-based reasoning in coordinate-based actions improves performance on complex embodied tasks.
Takeaways & Limitations
SpatialCoT cannot manage tasks requiring object rotation and relies on 2D visual inputs, motivating future exploration of 3D inputs.
Abstract
from arXiv · showhide
Spatial reasoning is an essential problem in embodied AI research. Efforts to enhance spatial reasoning abilities through supplementary spatial data and fine-tuning have proven limited and ineffective when addressing complex embodied tasks, largely due to their dependence on language-based outputs. While some approaches have introduced a point-based action space to mitigate this issue, they fall short in managing more intricate tasks within complex environments. This deficiency arises from their failure to fully exploit the inherent thinking and reasoning capabilities that are fundamental strengths of Vision-Language Models (VLMs). To address these limitations, we propose a novel approach named SpatialCoT, specifically designed to bolster the spatial reasoning capabilities of VLMs. Our approach comprises two stages: spatial coordinate bi-directional alignment, which aligns vision-language inputs with spatial coordinates, and chain-of-thought spatial grounding, which harnesses the reasoning capabilities of language models for advanced spatial reasoning. We evaluate SpatialCoT on challenging navigation and manipulation tasks, both in simulation and real-world settings. Experimental results demonstrate that our method significantly outperforms previous state-of-the-art approaches in both tasks.
1. Introduction
SpatialCoT addresses limits in language-based and point-based spatial reasoning by combining coordinate alignment with chain-of-thought spatial grounding. It is evaluated on challenging embodied planning tasks in simulation and the real world, including navigation and manipulation.
- Motivation: Most VLMs lack sufficient spatial information because they are primarily trained on 2D images paired with text.Additional spatial data and fine-tuning have mainly produced coarse-grained, language-based reasoning results.
- Motivation: Language-only commands can remain ambiguous for low-level policies when tasks require precise placement and collision avoidance.The paper illustrates this with rearranging a cup relative to a plate and other objects.
- Motivation: Point-based methods improve basic spatial reasoning but directly translating instructions into points bypasses VLM language-based reasoning.This limits performance on more challenging tasks requiring sophisticated or multi-step decisions.
- Approach: SpatialCoT uses spatial coordinate bi-directional alignment and chain-of-thought spatial grounding to improve fine-grained action generation.The approach explicitly leverages VLM reasoning capabilities rather than directly generating coordinate actions end to end.
- Evaluation: SpatialCoT achieves state-of-the-art results on challenging embodied planning tasks spanning navigation and manipulation.The evaluation uses closed-loop settings in simulators and the real world.
2. Related Work
Related work improves spatial reasoning through spatial data, point-based actions, and chain-of-thought planning, but these directions remain insufficiently connected for complex embodied actions.
- Spatial reasoning: SpatialVLM and SpatialRGPT enhance VLM spatial reasoning by collecting spatial question-answering data and fine-tuning models.These methods address limitations arising from predominantly 2D image-text training.
- Point-based actions: RoboPoint and RoboSpatial extend spatial reasoning toward fine-grained actions by predicting points that indicate locations or regions in images.RoboPoint calls this process spatial affordance prediction.
- Point-based actions: Point-based approaches primarily establish a direct mapping from language instructions to coordinates, neglecting VLM language-based reasoning.This limitation hampers performance on challenging tasks requiring intricate or multi-step reasoning.
- Chain-of-thought planning: Chain-of-thought methods guide models through logical steps instead of directly producing answers and have been explored for complex embodied tasks.Prior work has focused on language-based planning, while leveraging these thoughts for fine-grained actions remains largely unaddressed.
3. Our Method
SpatialCoT has two stages: bidirectional alignment between vision-language inputs and coordinates, followed by reasoning in language space that is grounded into coordinate-based actions.
- Spatial coordinate bi-directional alignment: The first stage equips VLMs to understand and generate coordinates through explicit spatial coordinate bi-directional alignment.The method aligns vision-language data with coordinates to support both coordinate understanding and coordinate generation.
- Spatial coordinate bi-directional alignment: The alignment stage trains both coordinate-to-language responses and language-to-coordinate outputs.Examples include identifying an object at a coordinate and generating coordinates for all objects matching an instruction.
- Spatial coordinate bi-directional alignment: Alignment data covers object understanding, affordance prediction, spatial relationships, and spatial compatibility.These groups connect language descriptions, possible actions, object layouts, and object compatibility.
- Chain-of-thought spatial grounding: The second stage generates a language-based rationale before translating that reasoning into coordinate-based actions.This uses VLM spatial and commonsense reasoning rather than directly producing coordinate actions.
- Chain-of-thought spatial grounding: A data-generation pipeline obtains rule-based simulator actions, annotates them on images, and uses a VLM to generate rationales.The pipeline is designed to collect consistent, high-quality rationale-action pairs for fine-tuning.
4. Experiments
The experiments evaluate SpatialCoT through closed-loop navigation and manipulation tasks, comparing it with multiple VLM baselines and analyzing difficulty levels, capabilities, and chain-of-thought effects. SpatialCoT improves performance across challenging embodied tasks, with especially strong gains in difficult manipulation and navigation settings.
- Experimental Setup: Closed-loop evaluation covers navigation and tabletop rearrangement manipulation tasks in simulators, with offline assessment of four fundamental VLM capabilities.The capability categories are object understanding, affordance prediction, spatial relationships, and spatial compatibility.
- Experimental Setup: Baselines include RoboPoint, Llama3.2V variants, direct action tuning, and GPT-4o, enabling comparisons across specialized, open-source, and closed-source models.The direct action tuning baseline is used to assess the proposed training method.
- Overall Results: 61.83% success rate and 3.33 distance gain show SpatialCoT outperforming direct action tuning and achieving the highest success rate among evaluated models.The distance gain is a 46% relative improvement over direct action tuning, while success rate increases by 4.43%.
- Overall Results: 82.6% success rate and 15.6% collision rate demonstrate improved manipulation performance over direct action tuning, which reaches 75.8% success and 21.3% collision.The reported end-to-end success-rate improvement is 6.8%.
- Difficulty-Level Analysis: +8.68% and +20.00% improvements occur on manipulation tasks with non-unique objects and many objects, while navigation level 4 improves by +8.66%.These settings involve crowded scenes, collision risk, sparse rewards, or greater distances.
- Capability Analysis: Object understanding and spatial relationships show a clear positive relationship with embodied planning success rates, while the other capability categories show positive but non-monotonic trends.The analysis examines how fundamental VLM capabilities relate to downstream task performance.
- Chain-of-Thought Analysis: Chain-of-thought improves use of spatial and contextual information, including room layout and commonsense knowledge, for locating objects accurately.In the alarm-clock case study, SpatialCoT infers the bedroom location before producing accurate results, unlike the baseline without CoT.
5. Limitations
SpatialCoT uses coordinate-based actions but does not support object rotations. It also relies on 2D visual inputs, motivating future exploration of 3D inputs for large spaces.
- SpatialCoT cannot manage tasks requiring object rotation because its coordinate-based action space does not account for rotations.
- SpatialCoT relies on 2D images, leaving 3D inputs for large spaces as a direction for future research.
6. Conclusion
The paper introduces SpatialCoT, a two-stage training approach that combines coordinate alignment with chain-of-thought spatial grounding. By anchoring VLM reasoning in coordinate-based actions, it improves performance on complex navigation and manipulation tasks.
- SpatialCoT enhances VLM spatial reasoning through spatial coordinate bi-directional alignment and chain-of-thought spatial grounding.
- The approach explicitly leverages VLM language-based reasoning while anchoring it in coordinate-based actions for complex embodied tasks.
- SpatialCoT outperforms previous methods on challenging embodied navigation and manipulation tasks.
7. Appendix
The appendix presents examples of spatial-coordinate alignment and chain-of-thought grounding data, along with task prompts and training-related tables. These examples cover object localization, navigability, spatial relations, collision prediction, and rationale-based navigation.
- Spatial coordinate alignment data includes prompts for generating collision-free object locations and recognizing objects at specified image coordinates.
- The alignment examples also cover navigability judgments, navigable-point generation, spatial-relation descriptions, and identifying objects relative to reference objects.
- Coordinate understanding data asks whether moving an object between two locations will cause a collision.
- Chain-of-thought grounding data contrasts navigation prompts without rationale against prompts requiring the model to provide its thinking before the final answer.
- The appendix includes tables for spatial coordinate bi-directional alignment data, chain-of-thought spatial grounding data, and model-training hyper-parameters.