New benchmark for robotic arm reach-avoid task using DRL

Researchers have developed a new benchmark for the reach-avoid task in robotics, utilizing the MuJoCo MJX physics engine and the Brax library for parallelized simulation and reinforcement learning. This benchmark aims to capture real-world complexities without simplifications, addressing limitations of previous DRL agents that performed well in simplified settings but failed in realistic scenarios. The study achieved state-of-the-art success rates of 96.1% for the UR5e robot and 98.8% for the Franka Emika robot in the reach task, and 86.8% and 95.2% respectively for the static reach-avoid task, highlighting that further research is needed for DRL to fully resolve this challenge. AI

IMPACT Establishes a more realistic benchmark for DRL in robotics, potentially accelerating progress in complex manipulation tasks.

RANK_REASON Academic paper detailing a new benchmark and experimental results. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• Brax
• Franka Emika robot
• MuJoCo
• reinforcement learning
• UR5e

• paper
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