Self-Learning AI for Adaptive Humanoid Robotics: Towards Generalized Autonomy
Kushal Khemani · 2025
The capability to acquire knowledge, evolve, and make inferences is crucial for human cognitive abilities.However, most humanoid robots remain constrained by pre-programmed behaviors, limiting their ability to function in dynamic, unstructured environments.The investigation unveils a self-learning AI framework designed to empower humanoid robots to autonomously learn and adapt to new tasks and environments without requiring significant retraining efforts.This research introduces SYNTH-RL, a self-learning AI framework that enables humanoid robots to rapidly adapt to new tasks and environments with minimal retraining.My novel approach with SYNTH-RL integrates Hierarchical Reinforcement Learning, Proximal Policy Optimization, Model-Agnostic Meta-Learning, Transformer-based Decision Models, and Multi-Modal Sensor Fusion to create a generalized self-learning architecture.Through HRL, high-level task planning is made possible, enabling AI to dynamically select goals like navigation, object manipulation, and obstacle avoidance.Low-level PPO policies execute precise motor control for real-time adjustments.To enhance situational awareness, I apply Multi-Modal Sensor Fusion, integrating Vision (CNNs), LIDAR, and Audio, along with Proprioceptive feedback.This allows robots to build rich spatial representations of their surroundings, visualizing and stimulating their senses.Transformer-based Decision Models improve adaptability by providing long-term memory and sequential reasoning, enabling robots to anticipate and react to environmental changes in real time.I validate this approach in MuJoCo simulations, which demonstrate a 90% reduction in adaptation time, enhanced locomotion stability, and improved efficiency compared to basic and mass-used reinforcement learning models.Sim-to-real transfer is accomplished utilizing domain randomization; this facilitates the smooth transition to physical humanoid robots for practical use in various real-world scenarios such as disaster response, industrial automation, and human-AI interaction.This study will also compare against existing benchmarks (e.g., SAC, TRPO, GATO, RT-2) to the research.The exploration in this field pushes forward the evolution of self-optimizing humanoid robotics, linking the disparity between rigid automation and authentically flexible, human-like intellect.