Complex Robotic Manipulation via Graph-Based Hindsight Goal Generation.

Area of Science:

• Robotics
• Artificial Intelligence
• Machine Learning

Background:

• Reinforcement learning algorithms like hindsight experience replay (HER) and hindsight goal generation (HGG) excel in multi-goal robotic manipulation with sparse rewards.
• Standard HGG struggles with obstacle-laden environments due to its reliance on Euclidean distance, which is inaccurate in such scenarios.
• Existing grid-based HGG requires handcrafted distance grids, limiting its automation potential for complex manipulation tasks.

Purpose of the Study:

• To introduce graph-based hindsight goal generation (G-HGG) as an automated solution for reinforcement learning in robotic manipulation tasks with obstacles.
• To enhance the applicability of HGG in environments where Euclidean distance is not a suitable metric for goal exploration.

Main Methods:

• Proposed graph-based hindsight goal generation (G-HGG), an extension of HGG that utilizes shortest path distances within an obstacle-avoiding graph.
• The environment is represented as a discrete graph to enable accurate distance calculations in the presence of obstacles.
• Evaluated G-HGG on four challenging robotic manipulation tasks incorporating obstacles.

Main Results:

• G-HGG demonstrated significant improvements in sample efficiency compared to both HGG and HER.
• The proposed method achieved higher overall success rates in manipulation tasks with obstacles.
• The graph-based approach effectively navigated environments with complex spatial constraints.

Conclusions:

• Graph-based hindsight goal generation (G-HGG) offers a more feasible and automated approach for solving robotic manipulation tasks with obstacles.
• G-HGG overcomes the limitations of Euclidean distance metrics in complex environments, leading to enhanced learning performance.
• The method shows strong potential for advancing reinforcement learning in real-world robotic applications with spatial challenges.