Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation (1703.00420v4)

Abstract: We present a learning-based mapless motion planner by taking the sparse 10-dimensional range findings and the target position with respect to the mobile robot coordinate frame as input and the continuous steering commands as output. Traditional motion planners for mobile ground robots with a laser range sensor mostly depend on the obstacle map of the navigation environment where both the highly precise laser sensor and the obstacle map building work of the environment are indispensable. We show that, through an asynchronous deep reinforcement learning method, a mapless motion planner can be trained end-to-end without any manually designed features and prior demonstrations. The trained planner can be directly applied in unseen virtual and real environments. The experiments show that the proposed mapless motion planner can navigate the nonholonomic mobile robot to the desired targets without colliding with any obstacles.

• The paper introduces a mapless motion planner using asynchronous deep reinforcement learning to map sparse sensor inputs directly to continuous steering commands.
• The approach achieves robust performance with smoother trajectories and superior adaptability in both simulated and real-world trials.
• The method significantly boosts sample efficiency and offers practical potential for deploying low-cost, map-independent mobile robots.

Overview of "Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation"

The paper by Lei Tai, Giuseppe Paolo, and Ming Liu presents a novel learning-based approach for mapless navigation in mobile robots using deep reinforcement learning (DRL). The research addresses the challenge of developing a motion planner that operates without reliance on a pre-built obstacle map—a pivotal concern in deploying mobile robots in unfamiliar environments.

Contributions and Methodology

This paper introduces a mapless motion planner that ingests 10-dimensional sparse range findings and the target's relative position as inputs. The system outputs continuous steering commands, optimizing the navigation process. Utilizing asynchronous deep reinforcement learning, the planner is trained end-to-end without manual feature design or prior demonstration.

Key contributions include:

• A novel mapless motion planner leveraging sparse laser range data.
• End-to-end training using an asynchronous version of the Deep Deterministic Policy Gradients (DDPG).
• Deployment capability in both virtual and real-world environments without fine-tuning.

Experimental Analysis

The experimental setup involved training a model in simulated environments using Turtlebot platforms and assessing performance in both virtual and real-world settings. Two distinct environments tested the model's adaptability and revealed differences in navigation efficiency based on environmental density and structure.

Numerical Results

1. Efficiency: The asynchronous DDPG method dramatically increased the sample collection rate compared to traditional DDPG, enhancing training efficiency.
2. Robustness: The planner successfully navigated through complex environments, demonstrating robustness to previously unseen obstacles and configurations.
3. Comparative Performance: Compared to a 10-dimensional Move Base strategy, the newly developed planner showed superior adaptability without interruptions, evidenced by smoother navigation trajectories in both simulation and real-world trials.

Implications and Future Directions

The implications of this research extend into multiple facets of autonomous robotics:

• Practical Applications: Reduced dependency on pre-built maps makes this approach suitable for low-cost indoor service robots, like those used for household cleaning.
• Adaptability: The method supports straightforward adaptation to other sensory inputs such as RGB or depth cameras, potentially enhancing perception capabilities in diverse operational contexts.

Potential future developments could explore integrating Long Short-Term Memory (LSTM) or Recurrent Neural Networks (RNN) into the architecture to handle dynamic environments more fluidly and anticipate long-term spatial changes. The progression of online learning strategies presents opportunities for continuous adaptation and improvement without human intervention or feature modification.

In conclusion, this paper provides significant insights and advancements in mapless navigation, emphasizing both the theoretical and application-oriented dimensions of DRL in robotics. It opens a path toward more flexible and robust navigation solutions in rapidly changing or unfamiliar spaces, highlighting the promising intersection of DRL with practical robotic applications.