Bridging the Gap: Sim-to-Real Transfer in Robotic Path Planning

Sim-to-real Transfer is an important process where models trained in a computer simulation are used in the real world. This transfer can be tough because the way things happen in a simulation might be quite different from how they happen in reality. This difference can lead to models that do not work as expected when they are deployed in actual environments.

In this piece, we are looking at the transfer of Reinforcement Learning (RL) agents focused on Coverage Path Planning (CPP). Coverage path planning is the task of directing a robot to cover every part of a particular area. This becomes tricky when the area is unknown, and the robot has to figure out the best path while mapping the space at the same time.

To tackle this problem, we created a semi-virtual environment. In this setup, we use some simulated elements like sensors and obstacles, but we also include real robot movements and time-sensitive factors. We studied how much fine-tuning is necessary to help the model fit the real-world environment. Our research revealed that a high frequency of model predictions helps narrow the gap between simulation and reality, while fine-tuning can sometimes cause initial performance issues.

The challenge of collecting enough real-world data to train machine learning models can be daunting. Gathering this data takes a lot of time and effort. Specifically, training RL agents for robots often requires access to the robot for the entire training duration. In the beginning stages of training, robots tend to make many mistakes, which can lead to hardware damage or require manual intervention. Using simulations is a more appealing option. However, transferring the knowledge gained in simulation to real-world scenarios is not easy due to differences in how things work.

Previous work has focused on minimizing the sim-to-real gap by improving the simulation environment. This could involve adding realistic noise, changing parameters randomly, or using other learning methods. Our goal is to take the best simulation-trained CPP models and apply them successfully in real environments.

In coverage path planning, the robot must find a way to cover all the usable space in an area. If the area is known, an optimal path can be planned ahead of time. If the area is unknown, the robot must figure out the path as it moves, which makes finding the best route more complicated. CPP is valuable in various robotic tasks, including lawn mowing, vacuum cleaning, search-and-rescue operations, and exploration.

Training RL models on physical robots requires extra considerations compared to simulation training. First, there is often a mismatch between the robot's behavior in the simulation and the real world, including movement and sensing differences. These discrepancies can lead to actions based on simulation that do not work well in reality. Second, because of inertia and system delays, the robot's movement does not follow a straightforward model. Lastly, since the robot is constantly moving during training, the robot’s perception of its environment may be outdated when it needs to decide on an action.

To ease the transition into the real world, we employ a real robot in a semi-virtual space. Here, we use a highly accurate positioning system within a controlled indoor area, along with a simulation that helps represent how the robot interacts with its surroundings. This approach allows us to manage real-time aspects of RL training more effectively while also giving us the freedom to create varied training settings without needing physical changes.

By fine-tuning the model in the real setting, we can bridge the gap to a fully realistic scenario. However, some additional real-world challenges still need to be addressed, such as uneven terrains and localization inaccuracies caused by navigation methods.

To minimize delays in the training process, we run model updates alongside state and action selections and handle all calculations on the robot’s onboard hardware. We opt for a soft actor-critic learning approach due to its efficiency when dealing with continuous actions. To deal with the non-linear dynamics, we include previous actions as part of the input observations.

Our findings indicate that while initial fine-tuning can hinder performance, maintaining a high inference frequency allows for successful transfers from simulation to reality, something that would otherwise require lengthy manual training.

We can sum up our contributions as follows:

1. We break the sim-to-real problem into two stages, introducing a real robot into a virtual environment as a bridge.
2. This method allows for the transfer of advanced RL strategies for CPP from simulation to actual robots.
3. We conduct data collection and model updates in parallel, enabling real-time adjustments without stopping the robot.
4. We assess how time steps and fine-tuning impact the real robot's performance.

#Related Work

The topics touched upon in this study relate to coverage path planning, the transition of models from simulation to real-world applications, and the training of RL in real time. Here we summarize relevant findings from previous research.

Coverage path planning methods can be broadly categorized into two groups: planning-based and learning-based. Planning-based methods break the area into smaller parts using techniques like cellular decomposition. Each part is then covered using a predetermined approach. Grid-based approaches, such as Spiral-STC, divide areas into smaller grids where a path is constructed to ensure each section is visited. Frontier-based methods select paths toward a point on the boundary of covered and uncovered areas.

Learning-based approaches apply machine learning techniques often in conjunction with planning methods to determine coverage routes. Reinforcement learning is a popular choice due to the sequential nature of these tasks. Certain studies have used RL to figure out the optimal order to cover defined areas. Other methods let RL decide the specific movements for the robot.

Sim-to-real transfer involves tackling the discrepancies in the way senses and movements are portrayed in simulations compared to real-life applications. Previous research has approached this problem through various means, which include randomizing physical parameters in simulations or adding noise to sensing and actuation processes. Another method is to utilize meta-learning techniques that enable rapid adaptation to novel tasks learned from multiple training scenarios. Imitation learning from expert demonstrations has also been effectively applied to coverage path planning.

When discussing robot control, some studies have managed to deploy RL policies for coverage tasks directly onto physical robots, using strategies that do not require fine-tuning. They allow a robot to identify the next point to approach while a separate module handles navigation, thus minimizing disparities between the simulation and reality. Other research efforts have involved transferring lightweight RL strategies alongside pre-trained perception systems, making use of real-world collected data to refine the simulation.

The differences between simulation and real-world training lead to various challenges. Tasks in a real environment involve action selection and policy updates, which require careful balancing, especially with the time constraints present in physical tasks. Several works have focused on conducting real-time RL and parallelizing aspects of data collection and model training to avoid interruptions during action selections and state evaluations.

In this section, we describe the CPP problem as a Markov decision process and outline the approach we used to train RL agents for coverage tasks.

#Problem Formulation

Our goal is to transfer an RL agent trained in simulation to the real world for coverage path planning in unknown environments. Here, the agent must map an area it has not encountered before and also find a path that covers all free spaces. A point is considered covered when it falls within the robot's coverage range and can be detected by the robot’s sensors.

The task can be structured as a partially observable Markov decision process (POMDP). In discrete time intervals, the agent predicts actions based on its observations of the state in order to maximize a reward system. The agent collects information about the environment geometry, already covered areas, and its own position to inform its actions. As it operates, the agent will only have access to the information it has observed, which limits its decision-making ability.

In the environment of a real robot, the expected behavior does not always fit the Markov property due to dynamic factors like inertia and momentum. This non-standard behavior complicates the task of predicting effective actions based on current state perceptions.

#Learning Coverage Paths with Reinforcement Learning

We utilize a special type of neural network called a scale-grouped CNN to predict control signals for coverage path planning. This network examines coverage areas, obstacles, and frontiers at multiple scales while tracking dense coverage and total variation rewards that influence planning decisions.

To represent the environment for the agent, we maintain global grid-like 2D maps showing covered areas and detected obstacles. By using local maps with different scales, the agent can be informed about both detailed and broader views of the area. Additionally, we create a frontier map that represents the borders between covered and uncovered regions, making it easier for the agent to navigate towards the uncovered areas.

To detect obstacles, we equip the agent with a simulated lidar sensor which measures distances and adds detected obstacles to the map. We also encode the lidar readings into the input space for the agent to process.

The robot we work with has two separately driven wheels, and the agent predicts both linear and angular speeds for movement. These speeds are then converted into movement for each individual wheel.

The reward system is critical for training. In addition to a basic reward for covering areas, we include penalties for variations in the coverage maps to encourage smoother coverage. This ensures the agent learns to cover spaces efficiently and reduces leftover areas that weren't attended to.

#Transferring CPP Agents from Simulation to the Real World

The developed approach allows for transferring the coverage path planning model from simulation to a semi-virtual environment. This transfer is crucial as training directly in real settings would require extensive manual input over long periods.

Even though we don't start training from the ground up in the real setting, we still require the ability to fine-tune the system using reinforcement learning.

Most common RL libraries only allow serial data collection and model updates. While this can work in simulations, it isn’t practical for real-time actions where the robot needs to keep moving. We need to wait for the next state after selecting an action, leading to delays in the robot's responsiveness.

To address these challenges, we design the training process to be broken down into four main steps: action selection, state selection, batch sampling, and model updating. In our experimental timeline, we measure the action selection and state selection processes to manage the time delays effectively.

In our system design, we opted for a thread model allowing simultaneous interaction with the environment and model updates. The model updating thread is structured to avoid conflicts while allowing rapid updates. By maintaining a separate version of the model for updates, we ensure that both threads can operate smoothly.

During this online training approach, we manage data collection and model training concurrently, making it easier to adjust the model based on real-time data while keeping the robot active.

#Smoothening the Sim-to-Real Gap

Even though we can quickly move into real environments with our strategy, fine-tuning often proves to be a cumbersome endeavor. Thus, we employ a semi-virtual setup where the real robot is used alongside simulated sensors, allowing for automatic training and testing.

This semi-virtual configuration maximizes the efficiency of RL training and eliminates some manual interventions. For example, if the robot were to drive toward a wall, both the robot and the environment can shift back into the center of the space, facilitating smooth operation.

To further bridge the sim-to-real gap, we enhance the simulation by factoring in inherent delays from motion and actions. By measuring the robot's acceleration and delays in action, we can adapt these elements into the simulated kinematic model, leading to a more realistic training experience. We also use past actions to inform current predictions, which helps to provide a clearer view of the robot's dynamics.

#Optimal Strategy for Going Sim-to-Real

When moving the CPP model from simulation to a semi-virtual environment, we can perform varying levels of adjustment and training that cater to the unique needs of real-world applications. Specifically, we can train used models without fine-tuning or with models that account for higher-order dependencies.

Key questions arise in terms of how models trained under different assumptions might perform when faced with real-world challenges. Our hypothesis suggests that models undergoing fine-tuning in the real world should outperform those that weren't fine-tuned.

In this section, we present findings from our experiments that focused on transferring advanced CPP policies to semi-virtual environments.

#Implementation Details

Our experimental setup involved training on a specialized robotic platform. The robot was equipped with high-performance computing hardware to execute the training algorithms effectively within a controlled indoor environment. The agent relied on a sophisticated motion capture system to track its movements accurately.

In simulation, we adopted the same fundamental setups and parameters of our training environment, ensuring consistency in the learning process. This allowed us to harness the benefits of reinforcement learning effectively while minimizing the discrepancies that might arise during the transfer.

During real-world training, we adjusted the learning rate and maintained the same time steps to facilitate a smoother transition. We also ensured that training involved fixed and randomized maps to evaluate the model's adaptability across different scenarios.

#Evaluation

To measure the effectiveness of the various RL models, we tracked the time it took to reach specified coverage levels during evaluations conducted on unseen maps. This assessment focused on understanding how well different models transferred their skills from simulation to real-world applications.

We found that policies trained with first-order assumptions were able to transfer effectively to a semi-virtual environment when deployed with high inference frequencies. However, fine-tuning processes sometimes led to a drop in performance, suggesting that additional training may be necessary to overcome challenges introduced by real-world dynamics.

In summary, our work highlights the importance of effectively managing the sim-to-real transfer for reinforcement learning agents focused on coverage path planning. We illustrate that through careful training strategies and effective management of the model's learning processes, it is indeed possible to achieve significant improvements in real-world applications while overcoming the inherent challenges presented by differences in environment dynamics.