Neural probabilistic motor primitives for humanoid control (1811.11711v2)

Abstract: We focus on the problem of learning a single motor module that can flexibly express a range of behaviors for the control of high-dimensional physically simulated humanoids. To do this, we propose a motor architecture that has the general structure of an inverse model with a latent-variable bottleneck. We show that it is possible to train this model entirely offline to compress thousands of expert policies and learn a motor primitive embedding space. The trained neural probabilistic motor primitive system can perform one-shot imitation of whole-body humanoid behaviors, robustly mimicking unseen trajectories. Additionally, we demonstrate that it is also straightforward to train controllers to reuse the learned motor primitive space to solve tasks, and the resulting movements are relatively naturalistic. To support the training of our model, we compare two approaches for offline policy cloning, including an experience efficient method which we call linear feedback policy cloning. We encourage readers to view a supplementary video ( https://youtu.be/CaDEf-QcKwA ) summarizing our results.

• The paper introduces a modular NPMP model that compresses thousands of expert motor policies for one-shot imitation.
• It leverages offline training with behavioral cloning and LFPC to achieve efficient policy transfer and robust performance.
• Experimental results show over 70% expert-level performance, signaling promising scalability and adaptability for humanoid control.

Overview of "Neural Probabilistic Motor Primitives for Humanoid Control"

This paper presents a novel approach to humanoid control by proposing a neural network-based framework termed Neural Probabilistic Motor Primitives (NPMP). The central aim is to develop a versatile motor module that can embody a wide range of high-dimensional behaviors for humanoid robots through a single learned model. The authors address the challenge of sequencing and generalizing individual skills, which has traditionally been a bottleneck in achieving flexible and adaptive control of complex physical structures like humanoids.

Key Contributions

1. Model Architecture: The proposed NPMP model encapsulates an inverse model architecture with a latent variable bottleneck, designed to perform robust one-shot imitation of whole-body humanoid behavior. It creates a compressed representation of expert policies in a dense motor primitive embedding space. This facilitates the construction of a shared policy space that allows the seamless integration of multiple motor skills.
2. Offline Training and Policy Cloning: Significant efforts are dedicated to training the model offline to compress thousands of expert policies. Two methods of offline policy cloning are explored: behavior cloning from noisy rollouts and Linear Feedback Policy Cloning (LFPC). The LFPC method is particularly novel, offering efficiency by requiring fewer expert interactions to achieve comparable performance.
3. Experimental Validation: The model demonstrates a remarkable ability to mimic unseen trajectories with high fidelity, achieving robust one-shot imitation. It also provides strategies for transferring and reapplying learned motor behaviors for new tasks, with movements exhibiting naturalistic qualities.

Empirical Findings

• Performance and Scalability: The NPMP system effectively encodes over 2700 expert policies, training with both LFPC and behavioral cloning strategies. LFPC notably reduces the computational cost while achieving competent transfer, highlighting the efficacy of linear feedback guides in complex policy cloning tasks. The model achieves a median performance of more than 70% of expert levels on test data, demonstrating substantial generalization capability.
• Impact of Regularization: Empirically, regularization parameters significantly influence the stability and performance of the NPMP system. Regularized models exhibited faster training convergence and higher performance, suggesting that regularization is crucial in balancing exploration-exploitation within the latent space.
• Latent Space Dynamics: The exploration of the latent space offered insights into the interpolation and sequencing of skills. Experimental results indicated that optimization within this space could further enhance the performance of certain behaviors, where direct one-shot imitation may initially underperform.

Implications and Future Work

This work extends the potential of humanoid robotic control by offering a scalable and efficient way to compress and reuse motor skills. The modularity and reuse potential of the NPMP system set a foundation for continual learning frameworks, where new skills can be integrated progressively without extensive retraining. The paper suggests that such architectures could support broader behavioral repertoires, advancing humanoid robotics closer to human-like adaptability and functionality.

Future work should aim to expand toward more diverse motor primitives, encompassing object interaction and manipulation tasks. Furthermore, exploring real-world applications and testing the architecture’s adaptability to dynamic environments remain critical for advancing practical deployability. Potential avenues for improvements include optimizing perturbation distributions for LFPC and extending the latent space to accommodate more complex or rare behaviors.

In conclusion, the NPMP framework presents a promising strategy for managing the complexity of humanoid control, merging the realms of efficient policy representation and flexible behavior execution. This work pushes the boundaries of current methodologies, paving the path for more intelligent and dexterous robotic systems.