Active Inference in Robotics and Artificial Agents: Survey and Challenges

Active inference originated from theories of brain function, explaining perception and action as inference processes minimizing surprise via predictive coding. Traditional control methods like Kalman filters and MPC excel in linear, predictable environments but struggle with complex, uncertain scenarios. Recent developments incorporate deep neural networks and hierarchical models, inspired by neuroscience, to handle multimodal perception and long-term planning. These advances aim to create robots capable of autonomous adaptation, exploration, and learning. Despite progress, challenges remain in real-time implementation, noise modeling, and scalability, motivating ongoing research into biologically plausible, scalable frameworks that unify perception, control, and learning.

The main challenge is enabling robots to perform robust state estimation and control in environments characterized by high uncertainty, sensor noise, and nonlinear dynamics. Existing algorithms often lack robustness, scalability, or biological plausibility, limiting their deployment in real-world scenarios. Achieving a unified framework that integrates perception, action, and learning—mirroring biological systems—is difficult due to computational complexity and the need for real-time performance. Additionally, handling multimodal sensory data and long-term planning under uncertainty remains an open problem, crucial for advancing autonomous capabilities.

This work introduces a hierarchical active inference framework that combines Variational Bayesian Inference with deep neural networks, supporting multimodal perception and future trajectory prediction. Key innovations include the application of Dynamic Expectation Maximization algorithms for continuous, robust state estimation, and the integration of deep learning modules for complex sensory data processing. The framework supports multi-task learning, enabling robots to adapt to diverse scenarios. Its biologically inspired design offers a unified approach to perception, control, and planning, surpassing traditional methods in robustness and scalability, and opening new avenues for autonomous system development.

• �� Construct generative models for environment dynamics and sensory data using deep neural networks, capturing complex relationships.
• �� Apply Variational Bayesian Inference to approximate the intractable posterior distributions, optimizing Variational Free Energy (F) via gradient descent.
• �� Design hierarchical model architecture to fuse multimodal sensory inputs and support long-term trajectory prediction.
• �� Implement Dynamic Expectation Maximization (DEM) algorithms for continuous, real-time state estimation, leveraging generalized coordinates.
• �� Integrate deep learning modules for perception and control, enabling end-to-end training.
• �� Use multi-task learning to improve generalization across diverse environments.
• �� Optimize computational efficiency through GPU acceleration and sparse representations.
• �� Validate through navigation and manipulation experiments on simulated and real robotic platforms, comparing with Kalman filters, RL, and MPC.

Experiments utilized datasets like KITTI for navigation and YCB for manipulation, benchmarking against Kalman filters, RL, and MPC. Metrics included success rate, control error, and robustness to sensor noise. Hyperparameters such as learning rate, model complexity, and noise assumptions were tuned via grid search. Ablation studies assessed the contribution of hierarchical structure and deep modules. Tests covered static and dynamic environments, with varying noise levels, to evaluate robustness and scalability. Real-world experiments validated simulation results, confirming improved accuracy and adaptability.

The AIF approach achieved an 85% success rate in navigation, outperforming RL (70%). Control error was reduced to 2.5mm, a 30% improvement over baseline controllers. Multimodal perception accuracy reached 92%, significantly higher than traditional filters. Robustness to sensor noise was demonstrated by maintaining performance under high noise levels. The hierarchical model supported stable long-term planning, with trajectory predictions accurate within 5% error over 10-second horizons. These results establish AIF as a promising paradigm for autonomous robotics.

Applicable to autonomous vehicles, industrial robots, and assistive devices, especially in uncertain, multimodal environments. Its robustness supports deployment in real-world scenarios like urban navigation, warehouse automation, and medical robotics. The framework enables robots to adaptively learn and plan, reducing reliance on pre-programmed behaviors. Future integration with reinforcement learning could further enhance goal-directed behavior, making autonomous systems more resilient and versatile.

Current models assume idealized noise characteristics; real sensor noise may deviate, affecting accuracy. Computational demands limit real-time deployment in high-dimensional spaces. Handling highly dynamic, non-stationary environments remains challenging, requiring online adaptation. Scalability to large multi-robot systems and long-term learning is still under development, necessitating more efficient algorithms and hardware support.