DRL enhances robot pose control with sim-to-sim-to-real strategy

Researchers have developed a deep reinforcement learning (DRL) approach to improve pose control for double-Ackermann-steering robots, addressing the challenge of transferring policies from simulation to real-world applications. Their method, ManeuverNet, was extended to handle full pose control and incorporated a sim-to-sim-to-real strategy to account for actuation uncertainties. By training with simulated actuation effects from Gazebo within a PyBullet environment, the DRL policies achieved a 92% success rate in Gazebo and a 69% success rate on a physical robot without further tuning. AI

IMPACT This research could improve the robustness and real-world applicability of DRL for robotic control systems.

RANK_REASON The cluster contains an academic paper detailing a novel research methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Double-Ackermann-steering mobile robots
• Gazebo
• ManeuverNet
• PyBullet

• paper
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →