NeRP: Neural Rearrangement Planning for Unknown Objects (2106.01352v2)

Abstract: Robots will be expected to manipulate a wide variety of objects in complex and arbitrary ways as they become more widely used in human environments. As such, the rearrangement of objects has been noted to be an important benchmark for AI capabilities in recent years. We propose NeRP (Neural Rearrangement Planning), a deep learning based approach for multi-step neural object rearrangement planning which works with never-before-seen objects, that is trained on simulation data, and generalizes to the real world. We compare NeRP to several naive and model-based baselines, demonstrating that our approach is measurably better and can efficiently arrange unseen objects in fewer steps and with less planning time. Finally, we demonstrate it on several challenging rearrangement problems in the real world.

• The paper introduces NeRP, a deep learning-based system for rearranging unknown objects using perceptual scene segmentation and graph encoding.
• It employs a modular architecture with graph encoder, selection, and collision networks to predict reliable pick-and-place operations.
• Benchmarked against model-based planners, NeRP achieves superior success rates and robust performance in both synthetic and real-world scenarios.

An Expert Overview of Neural Rearrangement Planning (NeRP) for Unknown Objects

In the field of robotics, the challenge of object manipulation in unstructured and dynamic environments is pivotal. The paper "NeRP: Neural Rearrangement Planning for Unknown Objects" offers a novel approach to tackle the rearrangement of unseen objects, which lacks reliance on pre-determined models. This is achieved by leveraging deep learning techniques to develop a robust multi-step planning system called NeRP (Neural Rearrangement Planning). This summary provides an analytical framework of the methodologies, evaluations, and implications presented in the paper, targeted toward advanced practitioners and researchers in AI and robotics.

Methodological Framework

NeRP is distinguished by its capacity to handle uncertainties and unknown scenarios by incorporating a graph-based approach paired with neural network models. The process begins with segmenting scene objects through a method known as Unknown Objects Instance Segmentation (UCN). Object identity and position are extracted as features using a pre-trained ResNet model, forming a perceptual representation of the scene as a graph. This graph is essential for grasping both the spatial layout and the semantic relationships of objects in a scene.

The core of NeRP consists of several interconnected modules:

1. Graph Encoder Network: Utilizes higher-order graph neural networks (k-GNNs) to encode complex relationships among objects. By moving beyond vanilla GNNs, higher-order interactions aid in capturing a comprehensive scene structure, critical for informed planning.
2. Object Selection and Placement Networks: These modules employ learned policies to select the sequence of pick-and-place operations. They utilize stochasticity through Dropout layers, ensuring robustness and adaptability to diverse configurations without requiring deterministic behavior.
3. Collision and Goal Satisfaction Networks: Employ deep learning models to evaluate the feasibility of actions in fulfilling the target configuration, ensuring collision-free movements and ultimate goal satisfaction.
4. Planning Algorithm: The framework uses simulated rollouts to predict downstream consequences of performed actions, aligning well with strategies like Model Predictive Path Integral (MPPI) control for motion execution.

Performance Evaluation

When benchmarked against model-based and heuristic planners, NeRP exhibits superior performance in success rate and efficiency, as evidenced in the quantitative measures on both synthetic and real-world unseen scenarios. The approach generalizes well to varying numbers and arrangements of objects, affirming its robustness and flexibility. Notably, the system demonstrates a planning capability comparable to that of a model-based expert planner, yet operates exclusively on perceptual data without predefined object models.

Implications and Future Directions

The development of NeRP suggests significant implications for embodied AI and robotic systems operating in dynamic, human-centric environments. By simplifying complex task planning to a series of reliable perceptual inputs and outputs, the NeRP framework advances the capability of robots to adapt and function autonomously in diverse settings.

Looking forward, several avenues present themselves for further exploration:

• Augmentation of Perception Systems: Enhancement of instance segmentation and feature extraction to mitigate perceptual errors that impact real-world application.
• Extending Planning to SE(3) Space: Broadening task complexity by including both translational and rotational motion planning to accommodate more challenging environments and tasks.
• Real-World Deployments: Ongoing experimentation with real-world setups to refine and validate the framework's robustness against practical challenges.

In conclusion, NeRP's major contribution lies in its provision of a flexible, learning-based approach to object rearrangement problems characterized by incomplete information, ensuring both theoretical advancement and practical applicability in robotics and AI.