Expanding Creative Possibilities: Exploring the Synergy Between Large Language Models (LLMs) and Theory of Inventive Problem-Solving (TRIZ)

by | Mar 12, 2023 | LLM | 0 comments

The Rise of Large Language Models (LLMs)

It seems that very soon the world will experience a significant change with the release of GPT-4, a new Artificial Intelligence system that is 500 times more powerful than the current version of GPT-3 (the core of ChatGPT). Unlike its predecessor, which has 175 billion machine learning parameters, GPT-4 boasts an impressive 100 trillion machine learning parameters and can process a variety of data types, such as videos, images, sounds, and numbers.

But what is GPT-X? GPT stands for “Generative Pre-trained Transformer”. It is a type of advanced artificial intelligence (AI) language model that uses deep learning techniques to generate human-like text. GPT models are pre-trained on large amounts of text data, and can then be fine-tuned for specific tasks, such as language translation or text summarization. The GPT models are based on the transformer architecture, which enables them to handle large amounts of context and generate coherent and meaningful text. The GPT models are widely used in various applications, such as natural language processing, chatbots, and virtual assistants.

But GPT-X (2, 3, 4) is part of the class of deep learning models called “Large Language Models” (LLMs). Large Language Models (LLMs) are advanced artificial intelligence (AI) language models that are capable of processing and generating human-like text at an unprecedented scale. These models are trained on massive amounts of text data and use deep learning techniques to learn the patterns and structures of human language. LLMs have been hailed as a breakthrough in AI, as they have enabled significant advancements in natural language processing and other language-based applications.  Some of the most known LLMs are:

  1. GPT-3 (Generative Pre-trained Transformer 3)
  2. GPT-2 (Generative Pre-trained Transformer 2)
  3. BERT (Bidirectional Encoder Representations from Transformers)
  4. RoBERTa (Robustly Optimized BERT approach)
  5. T5 (Text-to-Text Transfer Transformer)
  6. XLNet (eXtreme MultiLingual Language Model)
  7. ALBERT (A Lite BERT)
  8. ELECTRA (Efficiently Learning an Encoder that Classifies Token Replacements Accurately)
  9. BART (Bidirectional and Auto-Regressive Transformer).

For example, BART is a large-scale neural network-based language model that was introduced in 2019 by Facebook AI Research (FAIR). BART is a pre-trained LLM that can be fine-tuned for various natural language processing (NLP) tasks such as language translation, summarization, and question-answering. BART uses a combination of bidirectional and auto-regressive architectures, which allows it to generate high-quality text in a more efficient and effective manner compared to other LLMs. It is trained on a diverse set of tasks and can generate both summary and input-output pairs with equal effectiveness. BART’s architecture includes a denoising autoencoder, which allows it to learn from noisy input sequences, and a sequence-to-sequence model, which enables it to handle various text-generation tasks. It also includes a masked language modeling objective, similar to BERT, which allows it to understand the context of the text. BART has achieved state-of-the-art performance on a variety of NLP tasks, including summarization, question answering, and language generation. It has been used in various applications, such as chatbots, virtual assistants, and content generation tools.

The development of Large Language Models (LLMs) can be traced back to the early days of artificial intelligence research, but significant progress in this field has been made in recent years due to advancements in deep learning techniques and the availability of large amounts of data. The earliest LLMs were rule-based systems that used hand-coded linguistic rules to process text. However, these systems were limited in their ability to understand the nuances of human language and lacked the flexibility to adapt to different contexts. In the 1990s and early 2000s, statistical language models were developed, which used probabilistic models to predict the likelihood of a given word or phrase given its context. These models were able to achieve higher accuracy than rule-based systems, but still had limitations in their ability to handle complex language tasks.

The breakthrough in LLMs came with the development of deep learning techniques, which allowed for the training of neural networks on massive amounts of text data. In 2012, the development of the first deep learning language model, called the recurrent neural network (RNN), marked a significant step forward in the field. In 2018, the introduction of the first large-scale language model, called the Generative Pre-trained Transformer (GPT), revolutionized the field of natural language processing. The GPT model was trained on a massive amount of text data and was able to generate human-like text that was nearly indistinguishable from text written by humans.

Since then, numerous LLMs have been developed, such as GPT-2, GPT-3, BERT, and RoBERTa, among others. These models have demonstrated impressive performance on a range of language tasks, including language translation, text summarization, and sentiment analysis. Today, LLMs are being used in a wide range of applications, from chatbots and virtual assistants to intelligent search engines and content creation tools. They are expected to play an increasingly important role in shaping the future of AI and the way we interact with machines. In the figure below you can see some other LLMs.

Big tech companies think that scale is the “pragmatic” way to Artificial General Intelligence and will put LLMs on the pedestal in the AI race. LLMs are useful, but they failed in critical aspects without supervision. I am still wondering if scaling-up is the smart direction in AI model’s invention and innovation. “Superintelligence” is not linear … key aspects that have direct influence on AI are often ignored:

  • Training data are as important as model size
  • Cost-accuracy trade-offs lead to suboptimal trained models – a single training on GPT-3 is 12 M$
  • As LLMs got larger, current chips’ memory became insufficient to host, not even with parallelization techniques
  • Our brain is multisensory, and the world is multimodal, not only textual or lingual …
  • And they consume a lot of energy and emit CO2

With the current concepts of LLMs, Artificial Intelligence is too weak to make unsupervised decisions. AI creates value only if it increases the quality of decisions. Critical decisions impose:

  • Ability to predict effects of various scenarios
  • Judgment in the context, which cannot be reduced to data because of subjective aspects of the contextual factors

Great decisions are those who solve a problem without compromise. Current AI models are good to resolve problems in isolated applications, with minuscule spaces of maneuver. AI systems do the right job in a given space of constraints. AI fails in taking or replying impalpable human factors — the ethical, and other human concerns that guide the progression of human existence. AI still cannot “feel” subjective experiences, ethical and moral values without having some neuro-symbolic algorithms in place, and without human regulators in the loop.

TRIZ: The Systematic Approach to Problem Solving and Innovation

Transform the way you approach problem-solving and unleash your innovative potential with TRIZ – the revolutionary methodology that has helped solve some of the world’s most complex challenges.

I am going to tell you something that very few people know:

  • TRIZ has been used in many industries and has played a role in some of the world’s most significant discoveries and innovations.

  • One example of this is its application in the development of the Large Hadron Collider (LHC) at CERN, the world’s largest and most powerful particle accelerator. TRIZ was used in the design and development of the LHC to identify and solve complex technical problems, including issues related to magnet design and cooling systems.

  • Similarly, TRIZ has been applied by Samsung in the development of its high-end smartphones and other electronic devices, helping the company to innovate and stay ahead of the competition in a highly competitive industry. The use of TRIZ in these and other groundbreaking projects demonstrates the methodology’s effectiveness in solving complex problems and driving innovation in a wide range of industries.

  • TRIZ was used by NASA to develop a more efficient rocket engine for space exploration. By applying TRIZ, NASA was able to identify the root cause of inefficiencies in existing engine designs and develop a new engine that was more efficient, reliable, and cost-effective.

TRIZ (Theory of Inventive Problem Solving) is a problem-solving methodology that was developed in the former Soviet Union in the 1940s, and improved untill 1990s by Genrich Altshuller and his students and disciples. TRIZ is based on the idea that there are universal principles that underlie all inventive solutions and that these principles can be learned and applied to solve a wide range of problems.

Despite its effectiveness in solving complex problems and driving innovation, TRIZ is relatively unknown outside of certain industries and circles. There are several reasons why so few people know about TRIZ:

  • Limited Exposure: TRIZ is not widely taught in universities or other educational institutions, and there are relatively few training programs or courses available to the general public. As a result, most people have never heard of TRIZ and are unaware of its potential benefits.
  • Complexity: TRIZ can be complex and difficult to understand, particularly for people who are not trained in the methodology. The concepts and principles of TRIZ can be abstract and technical, making it difficult for laypeople to grasp.
  • Language Barriers: TRIZ was developed in the former Soviet Union and was originally published in Russian. The translation of TRIZ materials into English and other languages has been limited, which has made it less accessible to people outside of Russia and other former Soviet countries.
  • Limited Marketing: TRIZ has not been widely marketed or promoted to the general public, and there is little public awareness of its potential benefits. As a result, few people outside of certain industries or circles have heard of TRIZ.

TRIZ has been applied successfully in many industries, including manufacturing, aerospace, and electronics, among others. The methodology is based on a systematic approach to problem-solving that involves a set of tools and techniques for identifying the root cause of a problem, generating creative ideas for solving the problem, and selecting the best solution.

One of the key concepts in TRIZ is the “contradiction matrix,” which is a tool for identifying the contradictions that exist in a problem and suggesting potential solutions. The matrix is based on the idea that there are 39 possible contradictions that can exist in any problem, and each of these contradictions can be addressed using a set of inventive principles. The matrix provides a structured approach to problem-solving that helps to identify the most appropriate principle for addressing a specific contradiction.

Another important concept in TRIZ is the use of the “laws of technical systems evolution,” which are a set of principles that describe the way in which technical systems evolve over time. These principles can be used to identify the direction in which a system is likely to evolve and to anticipate future trends in technology.

TRIZ also emphasizes the importance of ideality, which is the concept of maximizing the benefits of a system while minimizing its drawbacks. Ideality is achieved by identifying the key functions of a system and finding ways to improve those functions while reducing the costs and drawbacks associated with the system. TRIZ has been particularly effective in solving complex problems that involve multiple conflicting objectives or constraints.

One of the strengths of TRIZ is its systematic approach to problem-solving. The methodology provides a structured approach to problem-solving that helps to identify the root cause of a problem, generate creative ideas for solving the problem, and select the best solution. This structured approach can be particularly useful in situations where problems are complex and difficult to understand.

Another strength of TRIZ is its emphasis on innovation. The methodology provides a set of principles for generating creative ideas that can be used to solve problems in new and innovative ways. This focus on innovation can be particularly valuable in industries where there is a high degree of competition and the need for continuous improvement and innovation is essential.

Despite its many strengths, TRIZ has some limitations. One limitation is that it can be complex and difficult to learn. The methodology requires a significant amount of training and practice to become proficient in its use. Another limitation is that TRIZ can be less effective in situations where problems are poorly defined or where there is a lack of data.

The Future of TRIZ and other Problem-Solving Tools in the Age of LLMs and the Raise of Super-LLM GPT-4

Revolutionizing Design: How TRIZ’s Systematic Problem-Solving Approach and GPT-4’s AI Capabilities Combine to Create a Powerful New Paradigm for Innovation.

With the rise of Large Language Models (LLMs) like GPT-3 and GPT-4, some people have argued that these AI technologies may make classical problem-solving tools like TRIZ and other techniques for creative thinking (e.g., ARIZ, USIT, SCAMPER, 6TH, FAST, AIDA, etc.) obsolete. However, this view overlooks the unique strengths and capabilities of these problem-solving methodologies and misunderstands the potential of LLMs in the creative process.

One reason for this misconception may be the impressive capabilities of LLMs in generating text and language-based tasks. LLMs can generate coherent and contextually relevant text, which can be helpful in some design and innovation contexts. However, LLMs are not designed to solve problems systematically or identify underlying principles, as TRIZ does. LLMs rely on patterns and correlations in large data sets, which can lead to biased or incomplete results.

Moreover, LLMs cannot replace human creativity and innovation. While they can generate new ideas, they cannot evaluate or refine them in the way that humans can. In contrast, classical problem-solving tools like TRIZ provide a structured approach to identifying and solving problems, using a set of principles and techniques that can be applied to a range of different contexts.

TRIZ and other creative thinking tools have unique strengths that complement and enhance the capabilities of LLMs. TRIZ helps to identify the root cause of a problem and provides a framework for evaluating potential solutions, while LLMs can generate new ideas and provide inspiration for designers and innovators. Classical problem-solving tools like TRIZ and other similar techniques still have value and can be used in conjunction with new AI technologies like GPT-3 or GPT-4. TRIZ, for example, provides a structured approach to identifying and solving problems, using a set of principles and techniques that can be applied to a range of different contexts.

While GPT-3 / GPT-4 can generate new design ideas and automate parts of the design process, it still requires human input to determine which ideas are relevant and useful. TRIZ and other classical problem-solving tools can help designers to evaluate and refine these ideas and to identify potential challenges or limitations that may not be immediately apparent.

Moreover, TRIZ and other similar tools can help designers to identify opportunities to leverage GPT-4’s capabilities in new and innovative ways. For instance, TRIZ can be used to identify ways to integrate GPT-4 with other technologies or to develop new approaches to using AI in the design process.

Therefore, rather than becoming obsolete or forgotten, classical problem-solving tools like TRIZ will likely continue to play an essential role in the design process, complementing new AI technologies like GPT-4 and helping designers to solve problems, generate new ideas, and create innovative solutions.

Let us first imagine how GPT-4 could be used by designers.

1. Generating Creative Ideas: GPT-4 can be trained on large datasets of existing designs, sketches, and artwork to generate new and innovative design concepts. By using natural language prompts, designers can explore many possible design variations, shapes, and colors quickly, thereby enhancing their creativity and imagination. Let us imagine some use cases:

  • Product Design: GPT-4 can be trained on large datasets of existing product designs to generate new and innovative product concepts. Designers can input a description of the desired product, and GPT-4 can generate multiple design variations that meet the given criteria, such as materials, features, and dimensions.

  • Graphic Design: GPT-4 can be trained on large datasets of existing graphic designs and artwork to generate new and innovative visual concepts. By using natural language prompts, designers, can explore many possible design variations, styles, and color schemes, thereby enhancing their creativity and imagination.
  • Architecture: GPT-4 can be trained on large datasets of existing architectural designs to generate new and innovative building concepts. Architects can input a description of the desired building, and GPT-4 can generate multiple design variations that meet the given criteria, such as space requirements, structural integrity, and aesthetic appeal.

  • Fashion Design: GPT-4 can be trained on large datasets of existing fashion designs to generate new and innovative clothing concepts. Designers can input a description of the desired garment, and GPT-4 can generate multiple design variations that meet the given criteria, such as fabric type, silhouette, and embellishments.

2. Enhancing Design Process Efficiency: GPT-4’s ability to process multiple data types, including videos, images, and sounds, can streamline the design process by reducing the time and effort required to create complex visual or auditory designs. Designers can use GPT-4 to generate designs, suggest design elements, and automate parts of the design process, freeing them up to focus on more creative tasks. Here are some examples of how GPT-4 can enhance the efficiency of the design process:

  • Video Game Development: GPT-4 can be trained on large datasets of existing game assets, such as characters, environments, and props, to generate new and innovative game content quickly. Designers can use GPT-4 to suggest new character designs, create new environments, and automate parts of the level design process, thereby saving time and increasing efficiency.

  • Audio Design: GPT-4’s ability to process sounds can be used to streamline the audio design process for film and video game production. Designers can use GPT-4 to generate sound effects, suggest music tracks, and automate parts of the sound design process, freeing them up to focus on more creative tasks.
  • Graphic Design: GPT-4 can be used to automate repetitive tasks in graphic design, such as creating backgrounds, patterns, and textures. Designers can use GPT-4 to generate design elements quickly, reducing the time and effort required to create complex visual designs.

  • Industrial Design: GPT-4 can be used to generate 3D models and designs for industrial products. Designers can input a description of the desired product, and GPT-4 can generate multiple design variations quickly, reducing the time and effort required to create complex product designs.

3. Improving Design Quality: With its vast database of machine learning parameters, GPT-4 can learn from existing designs and patterns to help designers identify design flaws or areas for improvement. It can provide real-time feedback on design choices, suggest design alternatives, and optimize design parameters for better performance. Here are some examples of how GPT-4 can improve design quality:

  • User Experience Design: GPT-4 can be trained on large datasets of user behavior and feedback to provide real-time feedback on user interface and experience design. Designers can input a design concept, and GPT-4 can suggest design alternatives, optimize design parameters, and provide feedback on potential design flaws, ensuring that the final design meets user needs and expectations.

  • Engineering Design: GPT-4 can be used to optimize engineering design parameters for better performance and efficiency. By analyzing large datasets of engineering design parameters, GPT-4 can suggest optimal design choices, identify design flaws or areas for improvement, and improve the overall quality of the final design.
  • Graphic Design: GPT-4 can be trained on large datasets of graphic design patterns and aesthetics to suggest design choices that are visually appealing and effective. By analyzing design patterns and aesthetics, GPT-4 can provide feedback on color choices, font selection, and layout design, helping designers to create designs that are visually appealing and effective.

  • Architecture: GPT-4 can be trained on large datasets of architectural design principles to suggest design alternatives and identify potential design flaws or areas for improvement. By analyzing design principles, GPT-4 can provide feedback on building materials, structural integrity, and design aesthetics, ensuring that the final design is functional, safe, and visually appealing.

4. Facilitating Collaborative Design: GPT-4 can enable collaborative design by allowing designers to share their work with the AI system, receive feedback, and work together to improve designs. This can help designers leverage the collective intelligence of the design community and accelerate the design process. Here we can imagine some examples of how GPT-4 can facilitate collaborative design:

  • Design Critique and Feedback: GPT-4 can be used to provide real-time feedback on design choices, allowing designers to collaborate and iterate on designs more efficiently. Designers can share their work with GPT-4 and receive feedback on design choices, suggestions for alternative designs, and optimization of design parameters, enabling them to work together to improve the quality of the design.

  • Co-creation of Designs: GPT-4 can be used to facilitate the co-creation of designs, allowing designers to work together to create new and innovative designs. Designers can input their design concepts into GPT-4, and the system can generate new design alternatives that incorporate elements from each designer’s work, helping to create a collaborative and inclusive design process.
  • Crowd-Sourced Design: GPT-4 can be used to analyze large datasets of design patterns and aesthetics, providing a platform for designers to share their work and receive feedback from a larger design community. By leveraging the collective intelligence of the design community, GPT-4 can facilitate the creation of innovative and high-quality designs.

  • Design Optimization: GPT-4 can be used to optimize designs based on feedback from multiple designers, enabling a more collaborative and iterative design process. Designers can input their design parameters into GPT-4, and the system can generate alternative design choices that take into account the feedback and suggestions of multiple designers.

Can GPT-4 alone be used to create complex engineering solutions?

While GPT-4 can generate new design ideas and automate parts of the design process, it is not designed to create complex engineering solutions on its own. GPT-4 relies on patterns and correlations in large data sets, which can lead to biased or incomplete results.

Engineering solutions often require a deep understanding of complex systems, physics, and mathematics. GPT-4 does not have this level of domain-specific knowledge and is not capable of solving engineering problems that require this knowledge.

Moreover, GPT-4 is an AI language model designed to generate human-like text and responses, rather than to create and optimize engineering solutions. While GPT-4 can be trained on engineering data, it is not a substitute for the domain-specific knowledge, expertise, and experience of engineering professionals.

So, we might say that while GPT-4 can be a valuable tool for generating new design ideas and streamlining parts of the design process, it should be used in conjunction with classical problem-solving tools like TRIZ and other engineering methodologies. These problem-solving methodologies have unique strengths and capabilities that complement and enhance the capabilities of GPT-4, helping designers and engineers to solve problems, generate new ideas, and create innovative solutions.

Let us now imagine the union of TRIZ with GPT-4.

We can imagine a plenty of ways to combine TRIZ and GPT-4 to create novel engineering solutions in inventive design, based on the strengths each part brings in the “problem solving equation”. TRIZ provides a structured approach to identifying and solving problems, using a set of principles and techniques that can be applied to a range of different contexts. GPT-4, on the other hand, provides a powerful AI capability to search in databases based on the TRIZ-driven principles and possibly proposing new design ideas, as well as to automate parts of the design process. By combining the strengths of TRIZ and GPT-4, designers and engineers can create more innovative and effective solutions to complex design challenges. For example, TRIZ can be used to evaluate and refine the design ideas generated by GPT-4, helping designers to identify potential flaws, limitations, and contradictions in the design. TRIZ can also provide a structured approach to identifying the root cause of a problem and developing solutions, which can be useful in guiding GPT-4’s generation of new design ideas. GPT-4 can be used to generate new and innovative design ideas quickly, using natural language prompts to explore many possible design variations, shapes, and colors. GPT-4 can also automate parts of the design process, freeing designers up to focus on more creative tasks and allowing them to explore many more design options than would be possible using traditional design methods. Moreover, TRIZ and GPT-4 can be used together to create hybrid problem-solving approaches that leverage the strengths of both methodologies. For example, TRIZ can be used to identify contradictions in the design, while GPT-4 can be used to generate new design alternatives that resolve these contradictions.

A very basic TRIZ-GPT-4 hybrid-problem solving algorithm is the following:

1. Identify the Problem: The first step is to identify the problem that needs to be solved. This can be done using TRIZ’s problem-solving methodology, which involves identifying the root cause of the problem, defining the ideal outcome, and analyzing the contradictions that are preventing the ideal outcome from being achieved.

2. Generate Design Ideas: Once the problem has been identified, GPT-4 can be used to generate new and innovative design ideas. Designers can input a natural language prompt that describes the problem and desired outcome, and GPT-4 can generate multiple design alternatives that incorporate different design elements and solutions.

3. Evaluate Design Alternatives: The design alternatives generated by GPT-4 can then be evaluated using TRIZ’s problem-solving methodology. Designers can use TRIZ to identify potential contradictions in the design alternatives and refine the design parameters to optimize the design’s performance.

4. Refine the Design: Once the design alternatives have been evaluated, designers can use GPT-4 to refine the design based on the feedback and suggestions provided by TRIZ. GPT-4 can generate new design alternatives that address the identified contradictions and optimize the design’s performance.

5. Select the Optimal Solution: The final step is to select the optimal solution based on the evaluation and refinement process. Designers can use TRIZ to evaluate the final design alternatives and select the one that best meets the design requirements and resolves any contradictions identified in the design.

Let’s Play a Bit!

Identify the Problem: Let’s say we are designing a robotic arm that needs to pick up and manipulate a variety of objects. However, we are encountering a problem where the robotic arm is not able to grip and hold certain objects securely.

Using TRIZ tools, we identify the root cause of the problem: Lack of grip strength combined with a lack of flexibility in the gripper design, and a lack of friction between the gripper and the object being held.

The ideal final result is a robotic arm that can grip and hold any object securely and reliably, without causing damage to the object or the robotic arm. The gripper should be able to adapt to different object shapes and sizes, provide sufficient grip strength, and maintain stability and control during manipulation.

In this case, the following contradictions can be identified:

  • Grip strength vs. flexibility: The gripper needs to be strong enough to hold objects securely, but also flexible enough to adapt to different object shapes and sizes.

  • Friction vs. damage: Increasing friction between the gripper and the object being held can improve grip strength, but may also increase the risk of damage to the object.
  • Stability vs. adaptability: The gripper needs to be stable and maintain control during manipulation, but also adaptable to different object shapes and sizes.

  • Strength vs. weight: The gripper needs to provide sufficient grip strength, but should not add too much weight to the robotic arm, as this may affect its overall performance.

Resolving these contradictions can help to achieve the ideal final result of the robotic arm that can grip and hold any object securely and reliably, without causing damage to the object or the robotic arm, and that is adaptable to different object shapes and sizes, provides sufficient grip strength and maintains stability and control during manipulation.

Generate Design Ideas: We can now use GPT-4 to generate design ideas to address this problem. We can input a natural language prompt such as “How can we improve the robotic arm’s grip on objects?” GPT-4 can generate multiple design alternatives, such as using different gripper designs or materials.

We ask the question and the AI model returns this:

  • One approach to improving the robotic arm’s grip on objects could be to use a different gripper design that is better suited to gripping a wider range of object shapes and sizes. For example, a multi-fingered gripper design that can adapt to different object shapes and sizes could be more effective than a single-fingered gripper that has a fixed shape.
  • Another approach could be to use a different material for the gripper that provides better friction and grip. For example, a rubber material could provide a better grip than a hard plastic material.

We continue asking: What are some innovative gripper designs or materials that could improve the robotic arm’s grip on objects?

The AI model returns the following answers:

  • Soft Grippers: Soft grippers are made from soft, flexible materials such as silicone or rubber, which allows them to conform to the shape of the object being gripped. This provides a more secure grip and reduces the risk of damage to the object.
  • Gecko-inspired Grippers: Gecko-inspired grippers use microscale hairs or adhesive pads that mimic the sticking power of gecko feet. These grippers can adhere to surfaces with high friction and provide a secure grip on a variety of objects.
  • Multi-fingered Grippers: Multi-fingered grippers have several independent fingers that can move and adapt to the shape of the object being gripped. This provides a more versatile grip that can accommodate a wider range of object shapes and sizes.
  • Magnetic Grippers: Magnetic grippers use strong magnets to grip metallic objects securely. This type of gripper can be useful for handling objects that are difficult to grip using other types of grippers.
  • Vacuum Grippers: Vacuum grippers use suction to grip objects securely. This type of gripper can be useful for handling objects with smooth, flat surfaces.

Now, we ask the AI system: Which of the above-mentioned ideas better fits the removal of the root cause?

The AI model replies:

“The multi-fingered gripper design would be the best fit to address the root cause of the problem which includes a lack of grip strength combined with a lack of flexibility in the gripper design, and a lack of friction between the gripper and the object being held. The multi-fingered gripper design provides several independent fingers that can move and adapt to the shape of the object being gripped, providing a more versatile grip that can accommodate a wider range of object shapes and sizes. Additionally, the fingers can be made from soft, flexible materials such as silicone or rubber, which allows them to conform to the shape of the object being gripped, providing a more secure grip and reducing the risk of damage to the object. Finally, the gripper can also incorporate materials that increase friction between the fingers and the object being gripped, further improving the grip strength and stability of the robotic arm.”

Now, we ask the AI system: How close is your proposal to the ideal final result of this problem?

The AI system returns the following message:

“The proposed solution of a multi-fingered gripper design incorporating soft, flexible materials and materials to increase friction is quite close to the ideal final result of a robotic arm that can grip and hold any object securely and reliably without causing damage to the object or the robotic arm. The multi-fingered gripper design can adapt to different object shapes and sizes, providing a more versatile grip. The soft, flexible materials used in the gripper design can help to reduce the risk of damage to the object being gripped. The incorporation of materials that increase friction can improve grip strength and stability, improving the robotic arm’s ability to hold objects securely. Finally, maintaining stability and control during manipulation is a key aspect of the ideal final result, which can be achieved through careful design and testing of the multi-fingered gripper design. Overall, the proposed solution is a good fit for achieving the ideal final result.”

Evaluate Design: Using TRIZ, we can evaluate the design alternatives generated by GPT-4. We can identify potential contradictions in the design alternatives, such as trade-offs between grip strength and flexibility, or between stability and adaptability. We can also analyze the design alternatives using TRIZ’s principles and techniques, such as the 40 Principles of Inventive Problem Solving, to refine the design parameters and optimize the design’s performance.

Let’s take the identified contradictions and show how the proposed solution of a multi-fingered gripper design can help to resolve them:

  • Grip strength vs. flexibility: The multi-fingered gripper design allows for a balance of grip strength and flexibility. The fingers can be made from soft, flexible materials such as silicone or rubber, which allows them to conform to the shape of the object being gripped, providing a more secure grip and reducing the risk of damage to the object. Additionally, the fingers can be designed to provide sufficient grip strength for a variety of objects, without sacrificing flexibility.
  • Friction vs. damage: The multi-fingered gripper design can incorporate materials that increase friction between the fingers and the object being gripped, improving grip strength without increasing the risk of damage to the object. The soft, flexible materials used in the gripper design also help to reduce the risk of damage to the object being gripped.
  • Stability vs. adaptability: The multi-fingered gripper design provides a balance of stability and adaptability. The fingers can be designed to maintain stability and control during manipulation, while also adapting to different object shapes and sizes.
  • Strength vs. weight: The multi-fingered gripper design can be designed to provide sufficient grip strength without adding too much weight to the robotic arm. The use of lightweight materials in the gripper design can help to minimize weight while still providing the necessary strength.

Now, we can take the TRIZ inventive principles that are associated with the contradictions in the problem under consideration:

  • Grip strength vs. flexibility:
    • Principle 3: Local Quality – Instead of trying to increase grip strength and flexibility in the entire gripper design, focus on improving the grip strength and flexibility in specific areas of the gripper, such as the fingers.
    • Principle 17: Another Dimension – Add a new dimension to the gripper design that can increase grip strength and flexibility. For example, the multi-fingered gripper design adds a new dimension of adaptability to the grip, allowing the fingers to adjust to different object shapes and sizes.
  • Friction vs. damage:
    • Principle 6: Universality – Use materials that can provide high friction without causing damage to the object being gripped. For example, the use of soft, flexible materials in the gripper design can help to increase friction without causing damage to the object.
    • Principle 15: Dynamicity – Use a dynamic approach to friction control. For example, the multi-fingered gripper design can incorporate materials that can adjust the level of friction based on the object being gripped, ensuring a secure grip without causing damage.
  • Stability vs. adaptability:
    • Principle 25: Self-Service – Design the gripper to be self-adapting, allowing it to adjust to different object shapes and sizes without compromising stability. For example, the multi-fingered gripper design allows the fingers to adjust to the object’s shape and size, maintaining stability and control during manipulation.
    • Principle 30: Flexible Shells and Thin Films – Use flexible materials that can provide both stability and adaptability. For example, the soft, flexible materials used in the multi-fingered gripper design provide both stability and adaptability, allowing the gripper to maintain control during manipulation while adjusting to different object shapes and sizes.

  • Strength vs. weight:

    • Principle 2: Taking Out – Remove unnecessary components or materials from the gripper design to reduce weight while maintaining sufficient strength. For example, the multi-fingered gripper design can be optimized to reduce weight while still providing the necessary strength.

    • Principle 28: Mechanics Substitution – Use a different mechanism or principle to achieve the desired strength without adding weight. For example, the use of soft, flexible materials in the multi-fingered gripper design can provide the necessary grip strength without adding significant weight.

Refine the Design: Based on the feedback and suggestions provided by TRIZ, we can refine the design using GPT-4. GPT-4 can generate new design alternatives that address the identified contradictions and optimize the design’s performance. For example, we can input a new natural language prompt such as “How can we improve the robotic arm’s grip on objects without sacrificing flexibility?” GPT-4 can generate new design alternatives, such as using a multi-fingered gripper design that can adapt to different object shapes and sizes. By comparing the proposed solution with the TRIZ principles we have a strong tool for verification of the quality of the proposed solution.

First, we apply the obtained inventive principles to the proposed solution by the AI system and we find the followings:

  • Grip strength vs. flexibility:

    • Applying Principle 3 (Local Quality): Focus on improving grip strength and flexibility in specific areas of the gripper, such as the fingertips or the areas that make contact with the object. This could involve using materials that are more flexible in these areas or adding features like bumps or ridges to increase grip strength.
    • Applying Principle 17 (Another Dimension): Consider adding a new dimension to the gripper design that can enhance grip strength and flexibility. For example, the addition of a vacuum or suction component to the gripper design could improve grip strength by creating a tighter seal between the gripper and the object.
  • Friction vs. damage:
    • Applying Principle 6 (Universality): Use materials that can provide high friction without causing damage to the object being gripped. For example, incorporating materials like soft rubber or silicone into the gripper design can provide a better grip without damaging delicate objects.
    • Applying Principle 15 (Dynamicity): Create a dynamic approach to friction control that adjusts to the object being gripped. For example, the use of smart materials or sensors could adjust the level of friction in real time based on the shape, size, and weight of the object being gripped.
  • Stability vs. adaptability:
    • Applying Principle 25 (Self-Service): Design the gripper to be self-adapting, allowing it to adjust to different object shapes and sizes without compromising stability. For example, the use of advanced materials like shape-memory alloys or programmable materials could allow the gripper to adjust and mold to different object shapes in real time.
    • Applying Principle 30 (Flexible Shells and Thin Films): Incorporate flexible materials into the gripper design that provide both stability and adaptability. For example, materials like polyurethane or elastomers can provide excellent grip strength while also being flexible enough to adapt to different shapes and sizes.

  • Strength vs. weight:

    • Applying Principle 2 (Taking Out): Remove unnecessary components or materials from the gripper design to reduce weight while maintaining sufficient strength. For example, designing the gripper with fewer fingers or a simpler mechanism can reduce weight while still providing enough strength to grip objects securely.

    • Applying Principle 28 (Mechanics Substitution): Use a different mechanism or principle to achieve the desired strength without adding weight. For example, using a magnetic gripper or a gripper that uses a high-pressure air stream could provide enough strength to grip objects securely without adding significant weight.

Select the Optimal Solution: Using TRIZ’s evaluation techniques, we can select the optimal solution that best meets the design requirements and resolves any contradictions identified in the design. For example, we may select a multi-fingered gripper design that optimizes grip strength and flexibility while maintaining stability and adaptability, but do not hurry with decisions!

Here we can generate potential improved solutions that incorporate the recommendations given by the TRIZ inventive principles in the initial proposed solution:

VARIANT 1: The gripper design will be a multi-fingered gripper made of soft, flexible materials like silicone or polyurethane. The gripper will have sensors and smart materials embedded within the fingertips to adjust friction levels in real-time based on the object being gripped. The gripper will also have a vacuum or suction component that can create a tighter seal between the gripper and the object, improving grip strength. The number of fingers in the gripper design will be optimized to reduce weight while still providing sufficient grip strength.To enhance stability and adaptability, the gripper design will use a self-adapting mechanism made of shape-memory alloys or programmable materials. This will allow the gripper to adjust and mold to different object shapes and sizes in real-time while still maintaining stability and control during manipulation. This improved solution incorporates the TRIZ inventive principles by using flexible materials, adding new dimensions like vacuum or suction components, and optimizing the number of fingers for weight reduction. The use of smart materials and sensors also creates a dynamic approach to friction control, while the self-adapting mechanism ensures both stability and adaptability.

VARIANT 2: The gripper design will be a hybrid gripper that combines the principles of a soft gripper and a gecko-inspired gripper. The gripper will be made of soft, flexible materials like silicone or rubber to provide a conforming grip on objects. It will also have microscale hairs or adhesive pads that mimic the sticking power of gecko feet, providing a secure grip on a variety of objects. To improve stability and control, the gripper will have an active feedback control system that adjusts the grip strength and friction levels based on the object being gripped. The feedback system will be powered by sensors and smart materials embedded in the gripper. To reduce weight and increase adaptability, the gripper will have a modular design that allows for the addition or removal of fingers as needed. This will allow the gripper to be customized for different object shapes and sizes, while also reducing weight and complexity.

VARIANT 3: The gripper design will be a vacuum gripper that uses a combination of suction and flexible materials to improve grip strength and adaptability. The gripper will be made of soft, flexible materials like silicone or elastomers, which will conform to the shape of the object being gripped, while the vacuum component will create a tighter seal between the gripper and the object. To improve stability and control during manipulation, the gripper will have an active feedback control system that adjusts the grip strength and friction levels in real-time based on the object being gripped. The feedback system will be powered by sensors and smart materials embedded in the gripper. To reduce weight and improve adaptability, the gripper will have a modular design that allows for the addition or removal of suction cups as needed. This will allow the gripper to be customized for different object shapes and sizes, while also reducing weight and complexity.

To select the best variant we can use several methods, such as Pugh, Combinex or others. Combinex is my favorite for engineering problems. Pugh is more for design problems, being no so accurate. In this example I will apply an even simpler tool. I will start to analyse the three variants using two criteria: cost and reliability. The conclusions are:

In terms of cost, the hybrid gripper (variant 2) and vacuum gripper (variant 3) are likely to be more cost-effective than multi-fingered gripper (variant 1). The hybrid gripper and vacuum gripper require fewer materials and components than the multi-fingered gripper, making them potentially less expensive to produce.

In terms of reliability, all three improved solutions incorporate feedback control systems that adjust the grip strength and friction levels in real-time based on the object being gripped. This ensures that the gripper maintains stability and control during manipulation, reducing the risk of damage to the object or the robotic arm. However, the vacuum gripper and multi-fingered gripper may be more reliable in gripping objects with irregular shapes, as they have more adaptable gripping surfaces.

So, if cost is a primary consideration, the hybrid gripper or vacuum gripper may be the better option. If reliability is the primary concern, the multi-fingered gripper or vacuum gripper may be the better choice, depending on the specific objects that need to be gripped. We can move further and add a new criterion: universality.

Universality is an important criterion to consider when selecting the best variant for a robotic gripper, as it determines how well the gripper can perform across a wide range of objects and scenarios. In terms of universality, the hybrid gripper and the vacuum gripper may be more universally applicable than the multi-fingered gripper. The hybrid gripper and vacuum gripper are designed to conform to a variety of object shapes and sizes, while also providing stable and controlled manipulation. The hybrid gripper’s microscale hairs or adhesive pads mimic the sticking power of gecko feet, making it a versatile gripper for a range of surfaces. Meanwhile, the vacuum gripper can adapt to objects with smooth, flat surfaces, which are often challenging for other types of grippers. In contrast, the multi-fingered gripper may be more specialized to particular object shapes and sizes due to its finger design. While the modular design allows for customization, it may not be as universally applicable as the other two solutions.

Now, considering all three criteria (cost, reliability, and universality), the hybrid gripper may be the best option, as it balances cost-effectiveness with versatility and adaptability. The hybrid gripper solution aligns well with the ideal final result of a robotic arm that can grip and hold any object securely and reliably, without causing damage to the object or the robotic arm. The gripper is designed to be adaptable to different object shapes and sizes, providing a secure and stable grip. The soft, flexible materials used in the gripper’s design also reduce the risk of damage to the object being gripped. Furthermore, the use of a feedback control system ensures that the gripper maintains stability and control during manipulation, further reducing the risk of damage. The gripper’s active feedback control system adjusts grip strength and friction levels in real-time based on the object being gripped, allowing for a customized and adaptable grip. In addition, the hybrid gripper solution incorporates the TRIZ inventive principles of using flexible materials and combining different gripper designs (in this case, a soft gripper and a gecko-inspired gripper) to create a more versatile and effective solution. In conclusion, the hybrid gripper solution aligns well with the ideal final result and utilizes TRIZ principles to create a more innovative and effective robotic gripper.

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Credits: Stelian Brad

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