Robotic Problem Solving Gets a Brain Boost

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Author: Denis Avetisyan

A new multi-agent system leverages the power of large language models to tackle complex robotic arm challenges with unprecedented accuracy.

Researchers introduce RoboSolver, a framework integrating LLMs and VLMs to create an intelligent educational assistant for robotics.

Despite advances in robotic control, bridging the gap between high-level task specifications and low-level motor commands remains a significant challenge. This paper introduces ‘RoboSolver: A Multi-Agent Large Language Model Framework for Solving Robotic Arm Problems’, a novel multi-agent system integrating large language and vision models to automatically analyze and solve complex robotic manipulation tasks. Our framework achieves state-of-the-art accuracy-up to 0.97-in forward and inverse kinematics, simulation, and control, significantly outperforming raw models across multiple benchmarks. Could this approach pave the way for more intuitive and accessible robotic programming interfaces, effectively democratizing access to advanced robotics capabilities?

The Evolution of Robotic Intelligence

Historically, robotic systems have functioned as extensions of deterministic machinery, executing pre-defined sequences of actions with limited capacity to respond to unforeseen circumstances. This reliance on meticulous pre-programming proves particularly problematic in real-world scenarios – environments characterized by unpredictable variables and constant change. A robot designed for a static assembly line, for example, struggles when presented with even minor deviations in part placement or orientation. Consequently, these systems often require complete restarts or human intervention when faced with novelty, severely limiting their utility in dynamic settings such as search and rescue operations, disaster response, or even complex household tasks. The inflexibility inherent in traditional robotics underscores the need for more sophisticated approaches capable of independent reasoning and adaptation.

The limitations of conventional robotics, reliant on rigid, pre-programmed sequences, are driving a significant evolution toward artificial intelligence as the core of robotic control. Increasingly complex tasks and unpredictable real-world scenarios demand a level of adaptability that traditional methods simply cannot achieve. Consequently, research and development are heavily focused on integrating AI, and notably, large language models (LLM), into robotic systems. These LLMs enable robots to interpret natural language instructions, reason about situations, and generate appropriate actions, fostering a degree of versatility previously unattainable. This paradigm shift promises robots capable of not just executing pre-defined tasks, but of learning, problem-solving, and collaborating with humans in more intuitive and effective ways, ultimately expanding their application across diverse industries and everyday life.

Deconstructing Complexity: The RoboSolver Framework

The RoboSolver Framework operates as a multi-agent system, distributing complex robotic tasks among multiple software agents to facilitate problem-solving. Each agent within the framework possesses specific capabilities and communicates with others to achieve a common objective. This collaborative architecture allows for decomposition of intricate challenges into manageable sub-problems, enabling parallel processing and improved efficiency. By distributing the computational load and leveraging specialized agents, the framework addresses limitations inherent in single-agent robotic systems when confronted with multifaceted tasks requiring diverse skillsets and environmental awareness.

The RoboSolver Framework distinguishes itself through the synergistic integration of Large Language Models (LLMs) and Vision Language Models (VLMs). LLMs are employed to parse and interpret high-level task instructions, converting natural language commands into structured representations suitable for robotic execution. Simultaneously, VLMs process visual input from the robot’s sensors, enabling environmental perception and object recognition. This combined approach allows the system to not only understand what is asked but also to see and interpret the context in which the task must be performed, bridging the gap between linguistic instruction and physical action.

The RoboSolver framework utilizes established robotics techniques, specifically forward and inverse kinematics, to convert perceived environmental data into actionable robotic movements. Forward kinematics calculates end-effector position and orientation given joint angles, while inverse kinematics determines the necessary joint angles to achieve a desired end-effector pose. These calculations, combined with computational tools for trajectory planning and control, allow the system to translate high-level instructions and visual input into precise motor commands. This process ensures accurate and controlled execution of tasks within the robot’s operational workspace, bridging the gap between perception and physical action.

Empirical Validation: Demonstrating Robust Performance

Benchmark testing of the RoboSolver Framework yielded a 97% accuracy rate in completing a suite of comprehensive robotic tasks. This performance represents a substantial improvement over existing traditional robotic systems, indicating a significant advancement in task completion reliability. The benchmark suite included varied challenges designed to evaluate the framework’s capabilities across multiple robotic functionalities, and the 97% accuracy was achieved through consistent performance across these diverse scenarios. This level of accuracy was determined by evaluating the framework’s outputs against known correct solutions for each task within the benchmark suite.

Evaluations demonstrate that GPT-4o consistently achieved a 93% accuracy rate in forward kinematics tasks. This performance exceeded that of both Claude-Sonnet-4.5 and DeepSeek-V3.2, with DeepSeek-V3.2 achieving a comparable 93% accuracy. These results were obtained through benchmark testing designed to assess the LLM component’s ability to accurately calculate robot joint angles required to reach a desired end-effector position, establishing GPT-4o as the highest-performing model within the tested group for this specific robotic task.

Performance validation indicates the RoboSolver Framework substantially improves task accuracy when compared to standalone Large Language Models (LLMs) and Vision-Language Models (VLMs). Specifically, the framework achieves a 67% increase in accuracy for forward kinematics tasks relative to the performance of a raw LLM. For tasks requiring visual input, the framework demonstrates a 20% higher accuracy than a raw VLM. These gains are attributed to the framework’s integrated approach to robotic problem solving, leveraging the LLM and VLM as components within a broader system designed for precise calculation and control.

The RoboSolver Framework incorporates established robotics principles to enhance movement precision. Specifically, the framework leverages the [latex] Jacobian Matrix [/latex] to relate joint velocities to end-effector velocities, enabling accurate control of robot motion. Velocity Computation determines the rate of positional change for each joint, while Acceleration Computation calculates the rate of change of velocity, facilitating smooth and controlled movements. These computations are integral to the framework’s ability to execute complex robotic tasks with a high degree of accuracy and responsiveness, moving beyond simple positional commands to dynamically adjust for environmental factors and task requirements.

Robot simulation played a critical role in validating the RoboSolver Framework’s performance across a diverse range of operational scenarios. Utilizing simulated environments, the framework underwent testing in conditions that would be impractical or unsafe to replicate physically, including variations in lighting, surface friction, and obstacle density. This comprehensive testing regime allowed for the identification and mitigation of potential failure points, ultimately verifying the framework’s robustness and reliability in handling unpredictable environmental factors. Specifically, simulation facilitated the execution of over 1,000 trials, each with randomized parameters, to confirm consistent performance metrics across varied conditions and to ensure the framework’s adaptability to real-world deployments.

Beyond Automation: Expanding the Scope of Robotic Potential

The RoboSolver Framework represents a significant leap in robotic problem-solving capabilities through the synergistic integration of Large Language Models (LLMs) and Visual Language Models (VLMs). Previously, robots relied on explicitly programmed instructions for specific tasks; this framework, however, allows robots to interpret natural language instructions and correlate them with visual input from their environment. This combination enables nuanced understanding – a robot can not only ‘see’ an object but also comprehend abstract requests like “move the red block to the left of the tall cylinder.” The framework doesn’t simply execute commands; it reasons about them, adapting to unforeseen circumstances and generalizing learned skills to novel situations, ultimately paving the way for robots capable of tackling complex, real-world challenges with greater autonomy and flexibility.

The RoboSolver framework leverages Elementary Transform Sequences (ETS) to achieve a detailed and adaptable understanding of robot movement and positioning. Rather than relying on complex, computationally expensive methods, ETS breaks down any robotic motion into a series of simple, fundamental transformations – like rotations and translations – effectively creating a ‘motion vocabulary’. This approach allows the framework to accurately represent a robot’s kinematics – its ability to move and manipulate objects – with greater efficiency and robustness. By encoding these basic movements, the system can then compose complex actions, plan trajectories in dynamic environments, and even recover from unexpected disturbances, all while minimizing computational load and maximizing adaptability to novel situations. This streamlined representation is crucial for enabling robots to perform intricate tasks with precision and reliability.

The advent of a versatile robotic problem-solving framework extends far beyond theoretical advancement, promising tangible benefits across multiple critical sectors. In industrial automation, this technology facilitates more adaptable assembly lines and quality control processes, reducing downtime and increasing efficiency through on-the-fly problem resolution. Search and rescue operations stand to gain from robots capable of navigating complex, unstructured environments and autonomously identifying potential victims, even in low-visibility conditions. Perhaps most profoundly, the framework’s potential in assistive robotics offers increased independence and quality of life for individuals with disabilities, enabling robots to perform intricate tasks and provide personalized support with minimal specialized programming – ultimately fostering a future where robots are truly collaborative partners in everyday life.

The RoboSolver Framework distinguishes itself through a marked reduction in the necessity for task-specific programming. Traditionally, deploying a robot in a new setting or assigning it a novel function demanded extensive, bespoke code tailored to that precise environment and objective. This framework, however, leverages integrated language and vision models to interpret task requests and translate them into actionable robot movements, effectively generalizing learned skills across diverse scenarios. Instead of explicitly coding each step, the system adapts to new challenges by reasoning about the task at hand and constructing solutions from a library of fundamental actions, dramatically lowering the barrier to robotic deployment and fostering a future where robots are readily adaptable to a wider range of real-world applications.

The development of RoboSolver underscores a principle central to effective system design: structure dictates behavior. This framework, built upon the interplay of Large Language Models and Visual-Language Models, demonstrates how a well-defined architecture enables complex problem-solving in robotics. Donald Davies observed, “If a design feels clever, it’s probably fragile.” RoboSolver avoids unnecessary complexity, prioritizing a robust and understandable multi-agent system capable of both forward and inverse kinematics. This simplicity isn’t a limitation, but rather the key to its adaptability and potential as an educational assistant, ensuring long-term viability over overly-engineered alternatives.

Future Directions

The elegance of RoboSolver lies not in its immediate problem-solving capability, but in the architecture’s potential for scalable intelligence. Current iterations demonstrate proficiency within constrained simulations, yet the true test resides in bridging this gap to physical systems. The inherent messiness of reality – imperfect sensors, unpredictable friction, the simple variance in manufactured parts – these are not bugs to be eliminated, but features to be accommodated. A system built on brittle precision will inevitably fracture; one that embraces inherent uncertainty will flourish.

The multi-agent approach, while promising, introduces its own set of complexities. Maintaining coherence and avoiding emergent, unintended behaviors within a collective of language models demands careful consideration of inter-agent communication and reward structures. It is not sufficient to simply increase the number of agents; the quality of their interaction dictates the system’s overall performance. The challenge, therefore, shifts from individual agent intelligence to collective wisdom – a surprisingly difficult problem, even for artificial minds.

Ultimately, the value of frameworks like RoboSolver extends beyond robotic manipulation. The principles of modularity, distributed reasoning, and adaptive learning are broadly applicable. The system represents a small step towards a more holistic understanding of intelligence, one where problem-solving is not merely a matter of computational power, but a consequence of well-defined structure and elegant interaction. The ecosystem, after all, is more resilient than any single organism.

Original article: https://arxiv.org/pdf/2602.14438.pdf

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

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2026-02-18 06:45