Robots Get a Brain: Planning Complex Tasks with Language and Vision

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Author: Denis Avetisyan

Researchers have developed a new framework enabling mobile robots to understand natural language instructions and visually perceive their environment to perform intricate object manipulation tasks.

Leveraging common sense reasoning from large language models, the system generates efficient and feasible pick-and-place plans – demonstrated through robotic navigation for a table-setting task – by adapting to complex environments and prioritizing both minimized travel cost and available workspace.
Leveraging common sense reasoning from large language models, the system generates efficient and feasible pick-and-place plans – demonstrated through robotic navigation for a table-setting task – by adapting to complex environments and prioritizing both minimized travel cost and available workspace.

LLM-GROP integrates large language models with computer vision to achieve semantically valid task and motion planning for mobile manipulation.

Achieving robust robotic manipulation in complex, real-world environments remains challenging due to the need for both high-level reasoning and low-level motion feasibility. This paper introduces LLM-GROP—a novel framework for visually grounded robot task and motion planning—that addresses this by integrating large language models with computer vision to enable mobile manipulation of multiple objects. LLM-GROP generates semantically valid plans and optimizes task and motion execution, leveraging common sense knowledge about object rearrangement. Could this approach bridge the gap between human-level adaptability and current robotic capabilities in dynamic environments?

Navigating Complexity: The Challenge of Mobile Manipulation

Traditional robotics struggles in unstructured environments, demanding integrated navigation and manipulation—Mobile Manipulation (MoMa). The core challenge lies in coordinating movement with precise object interaction. Successful MoMa requires robust Task and Motion Planning (TAMP), determining not only what a robot should do, but how. Existing TAMP methods often falter in real-world scenarios, relying on precise models that rarely hold true. A versatile MoMa system needs intelligence coupled with graceful adaptation to imperfect information.

Across all tableware arrangement tasks, LLM-GROP demonstrates superior performance, as evidenced by consistently higher user ratings and lower robot execution times when compared to three baseline methods.
Across all tableware arrangement tasks, LLM-GROP demonstrates superior performance, as evidenced by consistently higher user ratings and lower robot execution times when compared to three baseline methods.

Bridging planning and execution is difficult due to the explosion of possible actions and the need for continuous adaptation.

Semantic Understanding: LLM-GROP’s Approach

LLM-GROP introduces a framework integrating Large Language Models (LLMs) into the TAMP pipeline, enabling flexible and intuitive robotic manipulation, particularly in complex rearrangement scenarios. By leveraging LLMs, the system generates symbolic representations of desired object configurations from natural language instructions. This intermediate layer allows the system to understand and reason about tasks at a semantic level, interpreting ambiguous instructions and translating them into executable plans.

User ratings reveal that LLM-GROP consistently outperforms baseline methods across a range of object rearrangement tasks—from three objects (tasks 1-5) to five objects (task 8)—as indicated by the mean rating displayed for each task.
User ratings reveal that LLM-GROP consistently outperforms baseline methods across a range of object rearrangement tasks—from three objects (tasks 1-5) to five objects (task 8)—as indicated by the mean rating displayed for each task.

This decoupling of task specification from execution allows for greater adaptability and improved performance in object rearrangement.

Precision in Action: From Perception to Execution

LLM-GROP utilizes a ‘Visual Perception’ module with ‘Top-Down View Image’ data to localize objects and assess scene geometry, forming the basis for planning and execution. The system operates with limited prior knowledge, relying on real-time visual input. A core component is the generation of a ‘Motion Plan’ coupled with ‘Grasp Planning,’ optimized by ‘Standing Position Selection’ and ‘Navigation.’ A ‘Feasibility Evaluation’ verifies planned actions, while ‘Efficiency Optimization’ minimizes execution time.

In real-robot experiments, LLM-GROP achieved an 84.4% success rate, demonstrating its capacity for robust and adaptable robotic manipulation.

Beyond Current Limits: The Future of Intelligent Robotics

LLM-GROP represents a significant advancement in robotic reasoning by integrating LLMs with a framework for grounded reasoning about physical environments. This system moves beyond traditional approaches by incorporating ‘Common Sense Knowledge,’ allowing it to navigate ambiguity and uncertainty. The architecture facilitates a nuanced understanding of task requirements and environmental constraints.

The system reasons about spatial relationships and object affordances, enabling interaction with objects and unlocking potential for versatile robotic tasks. Human evaluators consistently rated LLM-GROP higher than baseline methods, highlighting improved performance and usability. This framework provides a foundation for robots capable of seamlessly interacting with humans, proving that true intelligence lies not in accumulation, but in essential reduction.

The pursuit of LLM-GROP embodies a reductionist principle; it distills complex manipulation goals into executable steps guided by language. This framework doesn’t strive for exhaustive pre-programming, but rather for a concise core capable of adaptation. As Alan Turing observed, “Sometimes people who are uncomfortable with computers think that computers are very logical, but actually, they’re terribly illogical.” LLM-GROP acknowledges this inherent ambiguity by leveraging the probabilistic reasoning of large language models, focusing on generating valid plans rather than striving for absolute, pre-defined perfection. The system prioritizes functional execution – a ‘just works’ solution – mirroring the idea that simplicity and clarity, not exhaustive detail, are the hallmarks of effective design. It’s a demonstration of how a minimal, well-defined structure can achieve surprisingly robust performance in a dynamic environment.

What Remains?

The pursuit of robotic autonomy, as demonstrated by frameworks like LLM-GROP, often feels like an exercise in increasingly elaborate scaffolding. Each added layer of semantic understanding, each refinement of task and motion planning, addresses a symptom, not the core ailment. The true limitation is not the robot’s ability to interpret instructions, but the inherent ambiguity within the instructions themselves. A perfectly obedient machine, executing a poorly conceived plan, remains a flawed endeavor.

Future work will inevitably focus on scaling these systems – larger language models, richer visual data, more complex environments. Yet, a more fruitful direction may lie in the opposite: deliberate simplification. Can a robot achieve more with fewer assumptions, fewer pre-programmed concepts of “common sense”? The challenge is not to teach the robot everything, but to define a minimal set of capabilities sufficient for meaningful interaction with an inherently unpredictable world.

Ultimately, the field must confront the paradox of intelligence. The more convincingly a robot mimics human cognition, the more glaringly obvious become the limitations of that imitation. True progress will not be measured by what the robot can do, but by what it doesn’t need to know.

Original article: https://arxiv.org/pdf/2511.07727.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-11-12 23:55