Pixels-to-Plans Neural Network for Tool Trajectory Estimation
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Solution Overview
Problem
Current robotic manipulation in unstructured environments faces challenges due to the brittleness of traditional sense-plan-act paradigms, particularly in estimating tool trajectories from raw sensor data, which suffers from high sample complexity and poor generalization performance.
Innovation Solution
A pixels-to-plans neural network is employed, comprising convolutional and fully connected layers, processing grayscale and depth images separately and combining features to estimate tool trajectories, with training using a labeled trajectory generated by a 3D motion planner and camera-to-tool calibration for accurate trajectory transformation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a traditional sense-plan-act paradigm is used to estimate tool trajectories, then the system can generate trajectories when complete 3D models and object pose are given, but the system becomes brittle due to the difficulty of generating precise 3D models and pose from input images
Solution Approach 1:
The patent introduces an intermediary neural network model that directly maps RGB-D images to tool trajectories, eliminating the need for intermediate 3D model generation and pose estimation steps. This direct mapping approach resolves the brittleness issue by removing the fragile interface between sensing and planning components.
Solution Approach 2:
The patent replaces the traditional mechanical sensing-planning pipeline with a neural network-based direct mapping system. The neural network learns to directly convert visual input (RGB-D images) into trajectory outputs, substituting the complex mechanical chain of 3D modeling and pose estimation with a single learned transformation function.
2Adaptability or versatility
If pixels-to-actions methods are used to directly estimate actions from sensor data, then the system avoids rigid interfaces, but the methods suffer from high sample complexity and poor generalization performance
Solution Approach 1:
The patent segments the pixels-to-actions approach by introducing an intermediate representation layer (spatial transformer network) that processes visual features before generating trajectories. This segmentation allows the system to learn from fewer samples by breaking down the complex direct mapping into manageable stages: image processing, feature extraction, and trajectory generation.
Solution Approach 2:
The patent changes the parameter space by transforming the direct pixels-to-actions mapping into a two-stage process with intermediate spatial transformer parameters. This parameter transformation enables better generalization by learning invariant features in the intermediate representation, reducing the sample complexity required for training.
3Device complexity
If a direct coupling of sensor input to controller actions is used in pixels-to-actions methods, then the system avoids intermediate representations, but the direct coupling is too restrictive and leads to bad generalization performance
Solution Approach 1:
The patent adds an intermediate dimensional layer (spatial transformer network) between sensor input and controller actions. This intermediate dimension processes visual features into a representation space that enables better generalization, transforming the direct 2D pixel-to-action mapping into a multi-dimensional pipeline that captures more robust features.
Data Source
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AI summary
An apparatus for estimating a trajectory of a tool may include: a memory storing instructions; and a processor configured to execute the instructions to: receive a task to be performed by the tool on a target object; receive a grayscale image and a depth image of the target object; and estimate a tool trajectory for performing the task, from the grayscale image and the depth image, via a pixels-to-plans neural network that is trained based on a labeled tool trajectory that is generated from a point cloud model of the target object.