Integrating Active Sensing and Rearrangement Planning for Efficient Object Retrieval from Unknown, Confined, Cluttered Environments

TL;DR

This paper introduces an integrated approach combining active sensing and Monte-Carlo Tree Search-based planning to efficiently retrieve objects from unknown, cluttered, confined environments, outperforming existing methods.

Contribution

It presents a novel integrated heuristic-based active sensing and MCTS-based retrieval planning framework for cluttered environment object retrieval.

Findings

Outperforms state-of-the-art methods in success rate

Reduces planning time and trials

Effective in both simulated and real-world scenarios

Abstract

Retrieving target objects from unknown, confined spaces remains a challenging task that requires integrated, task-driven active sensing and rearrangement planning. Previous approaches have independently addressed active sensing and rearrangement planning, limiting their practicality in real-world scenarios. This paper presents a new, integrated heuristic-based active sensing and Monte-Carlo Tree Search (MCTS)-based retrieval planning approach. These components provide feedback to one another to actively sense critical, unobserved areas suitable for the retrieval planner to plan a sequence for relocating path-blocking obstacles and a collision-free trajectory for retrieving the target object. We demonstrate the effectiveness of our approach using a robot arm equipped with an in-hand camera in both simulated and real-world confined, cluttered scenarios. Our framework is compared against various state-of-the-art methods. The results indicate that our proposed approach outperforms baseline methods by a significant margin in terms of the success rate, the object rearrangement planning time consumption and the number of planning trials before successfully retrieving the target. Videos can be found at https://youtu.be/tea7I-3RtV0.