Hierarchical Generative Modeling for Autonomous Robot Control Inspired by Human Motor Control

By Dr Silpaja Chandrasekar, PhDReviewed by Susha Cheriyedath, M.Sc.Nov 5 2023

In a paper published in the journal Nature Machine Intelligence, researchers explored how hierarchical generative modeling can mimic human motor control, enabling autonomous task completion. They examined nested timescales, highlighting the benefit of organizing global planning and local limb control hierarchically.

Through extensive physics simulations, they demonstrated that a humanoid robot embodying artificial intelligence (AI) completed complex tasks involving locomotion, manipulation, and grasping, even under challenging conditions. This study illustrated the effectiveness and feasibility of human-inspired motor control for autonomous, goal-directed tasks using a hierarchical architecture.

Challenges in Robotics for Human Motor Control

Human motor control involves complex coordination of body movements to achieve various objectives, such as navigating using a combination of limbs. This coordination occurs across nested timescales, where high-level plans guide lower-level reflexive limb movements. Researchers in robotics have actively applied hierarchical control systems to achieve a variety of motor behaviors, drawing inspiration from principles of human motor control.

Previous research in robotics has sought to emulate human-like capabilities in aircraft assembly and space missions by employing three primary approaches: human commands, planning methods, and learning approaches. Human commands lack autonomy, planning methods rely on environment models with limitations, and learning approaches face challenges in defining hierarchical structures for complex tasks.

Implementation of Hierarchical Generative Model

This approach involves the hardware implementation of the hierarchical generative model for autonomous robot control. It details the independent task performed by inverting the generative model, elaborating on the high-level decision-making, mid-level stability control, and low-level joint control. To showcase the model's capabilities in solving complex tasks requiring sequence and coordination of locomotion and manipulation, a specific task was designed. This task included four sub-tasks: picking up a box from one table, delivering it to another, opening a door, and walking to a destination. Successful task completion was enabled through a reward-based high-level and mid- and low-level policy, incorporating control strategies and imitation learning.

The hierarchical generative model for the robotics system consists of three levels: high-level decision-making, mid-level stability control, and low-level joint control. The structure, dependencies, and temporal aspects detail the model's factorization for active message passing between levels.

At the highest planning level, the robot determines the sequence of limb movements required to accomplish sub-tasks. Researchers actively employ deep reinforcement learning to learn the high-level decision-making policy, which, in turn, actively generates targets for mid-level stability control. Mid-level stability control includes manipulation and locomotion policies. Manipulation uses a model-predictive controller for the arms, while locomotion relies on a deep neural network policy learned through reinforcement learning for leg coordination.

The low-level joint controller uses impedance control, tracking desired common positions provided by mid-level stability control. Stiffness and damping parameters are adjusted to ensure accurate and compliant motion. The training process involves training each component separately, starting from the lowest level and proceeding to the high-level decision-making policy. Various tasks, including box delivery and door opening, penalty kick, and box transportation, are tackled through high-level policies with corresponding rewards and action spaces.

Researchers actively realize high-level decision-making through the training of a deep neural network to approximate the action-value function and actively select actions that yield the highest value in specific states. Mid-level stability control, consisting of manipulation and locomotion policies, involves the optimization of trajectories and joint positions. Manipulation uses a minimum-jerk model-predictive controller, while locomotion relies on reinforcement learning through Soft Actor-Critic (SAC).

Low-level joint control, based on impedance control, tracks target common positions and calculates desired torques to achieve precise joint motions. The detailed reward functions, state spaces, and action spaces for each task are provided, including a penalty kick, box transportation, and activating a conveyor belt task. The integration of these components demonstrates the practical implementation of the hierarchical generative model for autonomous robot control. For more in-depth technical information, please refer to the supplementary materials.

Validation of the Hierarchical Generative Model

The implicit hierarchical generative model enables a robot to learn and complete loco-manipulation tasks within a simulation autonomously. Validation took place in three distinct scenarios: (1) a sequential task involving moving a box and opening a door, (2) transporting a box between conveyor belts, and (3) executing a penalty kick with a football. The learned policy exhibited generality, robustness in the face of uncertainty, and adherence to the core principles of hierarchical motor control.

The high-level policy governs task-specific action sequences and communicates with lower levels responsible for limb coordination and joint control. This hierarchical model demonstrated adaptability to perturbations and external changes, such as obstacles, inclined surfaces, and even physical damage, like a missing foot.

The hierarchical control architecture adheres to critical principles of hierarchical motor control, including information factorization, partial autonomy, amortized control, multi-joint coordination, and temporal abstraction, operating at different timescales. This design ensures robust task performance and allows for fine-tuning specific sub-systems when performance issues arise, offering theoretical and biological insights into functional organization and modular structures.

The hierarchical generative model emulates the functional architecture of human motor control. At the lowest level, it replicates reflex-driven muscle contractions akin to spinal cord and brainstem functions. In an intermediate role, it mimics the cerebellum's fine-tuning of motor activities. The highest level corresponds to the cerebral cortex, which is responsible for deliberate planning and control. These hierarchical levels mirror the human motor control system and offer insights into robotics and cognitive neuroscience.

Conclusion

In conclusion, the work outlines a comprehensive hierarchical generative model for autonomous robot control, drawing inspiration from the human motor control system. The system encompasses high-level decision-making, mid-level stability, and low-level joint control, allowing robots to perform complex tasks through generative model inversion. Future work should focus on adaptability to unstructured environments, real-time performance, and human-robot interaction improvements. Refining the training process and hybrid approaches are also promising directions.