ActionReasoning: Robot Action Reasoning in 3D Space with LLM for Robotic Brick Stacking

• URL: http://arxiv.org/abs/2602.21161v1
• Date: Tue, 24 Feb 2026 18:07:06 GMT
• Title: ActionReasoning: Robot Action Reasoning in 3D Space with LLM for Robotic Brick Stacking
• Abstract summary: ActionReasoning is a framework that performs explicit action reasoning to produce physics-consistent, prior-guided decisions for robotic manipulation.<n>We instantiate this framework on a tractable case study of brick stacking, where the environment states are assumed to be already accurately measured.<n>Experiments demonstrate that the proposed multi-agent LLM framework enables stable brick placement while shifting effort from low-level domain-specific coding to high-level tool invocation.
• Score: 7.594306357823438
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Classical robotic systems typically rely on custom planners designed for constrained environments. While effective in restricted settings, these systems lack generalization capabilities, limiting the scalability of embodied AI and general-purpose robots. Recent data-driven Vision-Language-Action (VLA) approaches aim to learn policies from large-scale simulation and real-world data. However, the continuous action space of the physical world significantly exceeds the representational capacity of linguistic tokens, making it unclear if scaling data alone can yield general robotic intelligence. To address this gap, we propose ActionReasoning, an LLM-driven framework that performs explicit action reasoning to produce physics-consistent, prior-guided decisions for robotic manipulation. ActionReasoning leverages the physical priors and real-world knowledge already encoded in Large Language Models (LLMs) and structures them within a multi-agent architecture. We instantiate this framework on a tractable case study of brick stacking, where the environment states are assumed to be already accurately measured. The environmental states are then serialized and passed to a multi-agent LLM framework that generates physics-aware action plans. The experiments demonstrate that the proposed multi-agent LLM framework enables stable brick placement while shifting effort from low-level domain-specific coding to high-level tool invocation and prompting, highlighting its potential for broader generalization. This work introduces a promising approach to bridging perception and execution in robotic manipulation by integrating physical reasoning with LLMs.

Related papers

• Generalizable Geometric Prior and Recurrent Spiking Feature Learning for Humanoid Robot Manipulation [90.90219129619344]
  This paper presents a novel R-prior-S, Recurrent Geometric-priormodal Policy with Spiking features.<n>To ground high-level reasoning in physical reality, we leverage lightweight 2D geometric inductive biases.<n>For the data efficiency issue in robotic action generation, we introduce a Recursive Adaptive Spiking Network.
  arXiv  Detail & Related papers  (2026-01-13T23:36:30Z)
• Heterogeneous Robot Collaboration in Unstructured Environments with Grounded Generative Intelligence [54.91177026001217]
  Large language model (LLM)-enabled teaming methods typically assume well-structured and known environments.<n>We present SPINE-HT, a framework that addresses these limitations by grounding the reasoning abilities of LLMs in the context of a heterogeneous robot team.<n>Our framework achieves nearly twice the success rate compared to prior LLM-enabled heterogeneous teaming approaches.
  arXiv  Detail & Related papers  (2025-10-30T18:24:38Z)
• Executable Analytic Concepts as the Missing Link Between VLM Insight and Precise Manipulation [70.8381970762877]
  Vision-Language Models (VLMs) have demonstrated remarkable capabilities in semantic reasoning and task planning.<n>We introduce GRACE, a novel framework that grounds VLM-based reasoning through executable analytic concepts.<n>G GRACE provides a unified and interpretable interface between high-level instruction understanding and low-level robot control.
  arXiv  Detail & Related papers  (2025-10-09T09:08:33Z)
• Deploying Foundation Model-Enabled Air and Ground Robots in the Field: Challenges and Opportunities [65.98704516122228]
  The integration of foundation models (FMs) into robotics has enabled robots to understand natural language and reason about the semantics in their environments.<n>This paper addresses the deployment of FM-enabled robots in the field, where missions often require a robot to operate in large-scale and unstructured environments.<n>We present the first demonstration of large-scale LLM-enabled robot planning in unstructured environments with several kilometers of missions.
  arXiv  Detail & Related papers  (2025-05-14T15:28:43Z)
• Instruction-Augmented Long-Horizon Planning: Embedding Grounding Mechanisms in Embodied Mobile Manipulation [39.43049944895508]
  We present the Instruction-Augmented Long-Horizon Planning (IALP) system, which generates feasible and optimal actions based on real-time sensor feedback.<n>Our results demonstrate that the IALP system can efficiently solve tasks with an average success rate exceeding 80%.
  arXiv  Detail & Related papers  (2025-03-11T06:37:33Z)
• LLaRA: Supercharging Robot Learning Data for Vision-Language Policy [56.505551117094534]
  We introduce LLaRA: Large Language and Robotics Assistant, a framework that formulates robot action policy as visuo-textual conversations.<n>First, we present an automated pipeline to generate conversation-style instruction tuning data for robots from existing behavior cloning datasets.<n>We show that a VLM finetuned with a limited amount of such datasets can produce meaningful action decisions for robotic control.
  arXiv  Detail & Related papers  (2024-06-28T17:59:12Z)
• Grounding Language Plans in Demonstrations Through Counterfactual Perturbations [25.19071357445557]
  Grounding the common-sense reasoning of Large Language Models (LLMs) in physical domains remains a pivotal yet unsolved problem for embodied AI. We show our approach improves the interpretability and reactivity of imitation learning through 2D navigation and simulated and real robot manipulation tasks.
  arXiv  Detail & Related papers  (2024-03-25T19:04:59Z)
• InCoRo: In-Context Learning for Robotics Control with Feedback Loops [4.702566749969133]
  InCoRo is a system that uses a classical robotic feedback loop composed of an LLM controller, a scene understanding unit, and a robot. We highlight the generalization capabilities of our system and show that InCoRo surpasses the prior art in terms of the success rate. This research paves the way towards building reliable, efficient, intelligent autonomous systems that adapt to dynamic environments.
  arXiv  Detail & Related papers  (2024-02-07T19:01:11Z)
• Interactive Planning Using Large Language Models for Partially Observable Robotics Tasks [54.60571399091711]
  Large Language Models (LLMs) have achieved impressive results in creating robotic agents for performing open vocabulary tasks. We present an interactive planning technique for partially observable tasks using LLMs.
  arXiv  Detail & Related papers  (2023-12-11T22:54:44Z)
• Prompt a Robot to Walk with Large Language Models [18.214609570837403]
  Large language models (LLMs) pre-trained on vast internet-scale data have showcased remarkable capabilities across diverse domains. We introduce a novel paradigm in which we use few-shot prompts collected from the physical environment. Experiments across various robots and environments validate that our method can effectively prompt a robot to walk.
  arXiv  Detail & Related papers  (2023-09-18T17:50:17Z)
• VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models [38.503337052122234]
  Large language models (LLMs) are shown to possess a wealth of actionable knowledge that can be extracted for robot manipulation. We aim to synthesize robot trajectories for a variety of manipulation tasks given an open-set of instructions and an open-set of objects. We demonstrate how the proposed framework can benefit from online experiences by efficiently learning a dynamics model for scenes that involve contact-rich interactions.
  arXiv  Detail & Related papers  (2023-07-12T07:40:48Z)
• Transferring Foundation Models for Generalizable Robotic Manipulation [82.12754319808197]
  We propose a novel paradigm that effectively leverages language-reasoning segmentation mask generated by internet-scale foundation models.<n>Our approach can effectively and robustly perceive object pose and enable sample-efficient generalization learning.<n>Demos can be found in our submitted video, and more comprehensive ones can be found in link1 or link2.
  arXiv  Detail & Related papers  (2023-06-09T07:22:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.