Viewpoint Push Planning for Mapping of Unknown Confined Spaces (2303.03126v2)

Abstract: Viewpoint planning is an important task in any application where objects or scenes need to be viewed from different angles to achieve sufficient coverage. The mapping of confined spaces such as shelves is an especially challenging task since objects occlude each other and the scene can only be observed from the front, posing limitations on the possible viewpoints. In this paper, we propose a deep reinforcement learning framework that generates promising views aiming at reducing the map entropy. Additionally, the pipeline extends standard viewpoint planning by predicting adequate minimally invasive push actions to uncover occluded objects and increase the visible space. Using a 2.5D occupancy height map as state representation that can be efficiently updated, our system decides whether to plan a new viewpoint or perform a push. To learn feasible pushes, we use a neural network to sample push candidates on the map based on training data provided by human experts. As simulated and real-world experimental results with a robotic arm show, our system is able to significantly increase the mapped space compared to different baselines, while the executed push actions highly benefit the viewpoint planner with only minor changes to the object configuration.

• The paper presents Viewpoint Push Planning, a novel framework utilizing deep reinforcement learning and targeted push actions to enhance mapping efficacy in unknown confined spaces.
• The system integrates DRL for optimal viewpoint selection based on initial views and spatial entropy, combined with learned push actions for minimally invasive exploration of occluded areas.
• Experimental evaluation demonstrates superior entropy reduction compared to state-of-the-art methods and highlights the framework's real-time feasibility and effectiveness in reducing object displacement during pushes.

Overview of "Viewpoint Push Planning for Mapping of Unknown Confined Spaces"

This paper presents a novel approach, dubbed "Viewpoint Push Planning," for enhancing the mapping of confined spaces with unknown configurations, such as shelves populated with obstructed objects. Traditional viewpoint planning (VPP) challenges are accentuated in confined settings due to frequent occlusions and limited feasible perspectives. The authors propose an innovative framework leveraging deep reinforcement learning (DRL) to produce optimized viewpoints, thus minimizing map entropy. Additionally, they integrate strategic push actions to uncover obscured areas without causing significant disruptions to object arrangements.

Methodology

The proposed methodology comprises several key components:

• State Representation: A 2.5D occupancy height map is used as the state representation. This format is efficiently derived and updated from point clouds generated by an RGB-D camera located on the robot's end effector. This representation strikes a balance between granularity and computational efficiency, aligning with the constraints of confined spaces.
• Viewpoint Planning (VPP): The model utilizes DRL to autonomously identify the optimal viewpoints that reduce mapping entropy. It trains the agent to select promising viewpoints that maximize scene visibility based on initial views, spatial entropy, and the number of spatially predefined configurations.
• Push Prediction Network: Minimally invasive push actions are generated by a neural network which learns from human-labeled data. This network advises non-prehensile manipulations targeted at effectively repositioning obstructions while keeping object displacement minimal—an essential requirement to preserve the natural configuration of sensitive environments.
• Action Selection Strategy: The system is designed to balance between VPP and push actions. It autonomously decides, based on the decrease in entropy and potential scene alterations, whether to move to the next best viewpoint or execute a push.

Experimental Evaluation

The framework was extensively evaluated both in simulated environments and real-world settings using a robotic arm equipped with a depth camera. Key findings include:

• Entropy Reduction: In comparison to both random and sample-based view planners, the proposed system achieved superior entropy reduction, with significant improvements over state-of-the-art methods such as RSE and GMC.
• Mapping Efficacy and Push Effectiveness: The integration of push actions led to additional entropy reduction post-VPP. Notably, the learned push actions reported lower object displacement and a decreased incidence of object drop-offs, underscoring their targeted minimally invasive nature.
• Real-Time Feasibility: The operational efficiency of the framework was highlighted through its rapid planning and execution times, reinforcing the potential for real-world applications in dynamically constraining environments.

Implications and Future Directions

The findings of this research hold several practical implications:

• Enhanced Robotics in Confined Environments: The ability to effectively map constrained environments with minimal disruptions enhances the utility of robotic assistance in domestic, industrial, and retail settings.
• Interactive Perception: The integration of interactive perception, balancing between perception enhancement and scene stability, demonstrates potential extensions in scenario adaptability and dynamic response.
• Future Optimizations: Further enhancements might explore adaptive DRL methods that optimize push action prediction and viewpoint decision-making in more diverse and complex object configurations.

In conclusion, the Viewpoint Push Planning framework represents a substantial advancement in navigational and mapping capabilities within confined environments, integrating cutting-edge DRL with interactive sensing to overcome traditional challenges of occlusion and limited spatial viewpoints. Future work may extend these approaches to cover more complex, dynamic, and larger-scale setups.