Sim-to-Real Transfer for Muscle-Actuated Robots via Generalized Actuator Networks

1. 1
   Understand Muscle Robot Challenges

   Familiarize yourself with the inherent complexities of muscle-actuated robots, including severe nonlinearities, friction, and hysteresis, which hinder accurate modeling and control. Recognize these as the core problems for sim-to-real transfer.
2. 2
   Explore Generalized Actuator Networks (GANs)

   Research and understand the principles behind Generalized Actuator Networks. These are neural network architectures designed to learn and model complex, non-linear dynamics of actuators, effectively bridging the simulation-to-reality gap.
3. 3
   Design Data Collection Strategy

   Plan how to collect input-output data from your robot's actuators, both from simulation and, if possible, from real-world experiments. Focus on capturing various operational conditions to cover the actuator's dynamic range and complexities.
4. 4
   Implement a GAN Architecture

   Develop a neural network model, serving as your Generalized Actuator Network. This network should be capable of taking actuator commands/states as input and predicting corresponding physical outputs/states, learning the complex mapping.
5. 5
   Train and Validate the Network

   Train your GAN using the collected simulated and real-world data. Focus on metrics that indicate accurate prediction of actuator behavior across different conditions. Validate its performance against unseen data to ensure generalization.
6. 6
   Integrate for Sim-to-Real Transfer

   Integrate the trained Generalized Actuator Network into your robot's simulation environment or control system. Use its learned dynamics to refine simulated behavior or directly compensate for real-world discrepancies, improving sim-to-real transfer.

import torch
import torch.nn as nn

class GeneralizedActuatorNetwork(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(GeneralizedActuatorNetwork, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(hidden_size, hidden_size)
        self.fc3 = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        out = self.fc1(x)
        out = self.relu(out)
        out = self.fc2(out)
        out = self.relu(out)
        out = self.fc3(out)
        return out

# Example usage:
# input_dim = 10 # e.g., motor command, current state, desired state
# hidden_dim = 64
# output_dim = 2  # e.g., predicted force, velocity
# model = GeneralizedActuatorNetwork(input_dim, hidden_dim, output_dim)
# print(model)

• Paperarxiv.org