Quality Diversity under Sparse Interaction and Sparse Reward: Application to Grasping in Robotics.

Area of Science:

• Robotics
• Artificial Intelligence
• Machine Learning

Background:

• Quality-Diversity (QD) methods excel in generating diverse, high-performing solutions, primarily in locomotion tasks.
• Robotic grasping remains a significant challenge due to reward sparsity, behavioral sparsity, and behavior space misalignment.

Purpose of the Study:

• To investigate the efficacy of Quality-Diversity methods in addressing the complexities of robotic grasping.
• To evaluate QD approaches on diverse grasping domains and robot-gripper setups.

Main Methods:

• Conducted experiments using 15 different QD methods across 10 grasping domains.
• Utilized 2 distinct robot-gripper configurations and 5 standard objects for comprehensive testing.
• Focused on variants of the MAP-Elites algorithm, prioritizing successful solution selection.

Main Results:

• MAP-Elites variants demonstrated superior performance compared to other methods across key metrics.
• Experimental evidence suggests that sparse interactions can induce deceptive novelty.
• Achieved unprecedented efficiency in generating grasping trajectories.

Conclusions:

• Prioritizing successful solutions within QD frameworks is highly effective for robotic grasping.
• QD methods can overcome significant challenges in sparse reward and behavior spaces.
• This work presents a novel and efficient approach to generating grasping trajectories.