The Figure 01 breakthrough, while significantly enhanced by collaboration with OpenAI, could theoretically reach impressive levels of autonomy and capability through several key achievements in robotics and artificial intelligence.

OpenAI played a crucial role in the development of Figure 01 by providing advanced artificial intelligence technologies and expertise that significantly enhanced its capabilities. This collaboration facilitated natural language processing, allowing Figure 01 to understand and respond effectively to human language, which is essential for meaningful interactions. OpenAI's advancements in reinforcement learning improved the robot's decision-making abilities, enabling it to learn from experiences and optimize performance over time. Additionally, OpenAI's sophisticated neural network architectures enhanced Figure 01's perception and understanding of its environment, while their research in imitation learning supported the robot's ability to mimic human actions for greater dexterity and adaptability. Furthermore, OpenAI's commitment to ethical AI practices ensured that Figure 01 adhered to safety protocols and responsible usage. Overall, this partnership positioned Figure 01 at the forefront of technological advancements in robotics and artificial intelligence.

For Figure 01 to operate independently without OpenAI, it would need to have robust machine learning algorithms developed in-house. This would involve creating sophisticated neural networks capable of learning from vast amounts of data without relying on external models. These algorithms would need to excel in pattern recognition, reinforcement learning, and natural language processing, ensuring the robot can understand and respond to its environment effectively.

The robot would require advanced sensory systems that integrate visual, auditory, and tactile data seamlessly. This means developing highly sensitive cameras, microphones, and touch sensors that allow Figure 01 to perceive its environment accurately. The integration of these sensory inputs would enable the robot to make informed decisions based on real-time information, similar to how humans interact with their surroundings.

While imitation learning is already a cornerstone of Figure 01's development, achieving independence would necessitate refining these techniques further. The robot would need to learn not only from observation but also from limited demonstrations or even trial and error. By developing advanced algorithms that allow for faster and more efficient learning, Figure 01 could adapt to new tasks without relying on external teaching methods.

Figure 01 would need the ability to tackle complex problems autonomously. This could involve developing advanced reasoning capabilities that allow the robot to analyze situations, identify potential solutions, and implement them effectively. Such problem-solving skills would require a strong foundation in cognitive computing, enabling the robot to think critically and make decisions in dynamic environments.

To reduce reliance on external data sets, Figure 01 could benefit from advancements in self-supervised learning. This approach allows the robot to generate its training data by interacting with its environment, thus improving its capabilities through experience. By leveraging self-supervised learning techniques, the robot could continuously improve its performance without the need for external input.

To engage in meaningful interactions with humans, Figure 01 would need to develop advanced HRI systems that allow for natural communication. This includes understanding context, emotions, and social cues, which would enable the robot to respond appropriately in various situations. Achieving this level of understanding would require significant advancements in natural language processing and emotional intelligence.