Object Trajectory Keypoints for Uncertainty-Aware Robot Interaction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional systems for predicting object motion in 3D environments fail to account for the inherent non-determinism and uncertainty in environments with unpredictable elements, leading to inaccurate and inefficient predictions, and existing deterministic models fail to address the challenges of stochastic environments with bifurcation points.
Innovation Solution
A motion prediction model that accounts for uncertainty and non-determinism by using a piecewise representation of object trajectories, trained on an ensemble of forward models to identify keypoints and measure uncertainty, and integrates a machine learning model to control agents like autonomous robots to interact with dynamic objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If deterministic models are used to predict object motion, then the system complexity is reduced, but the prediction accuracy deteriorates in stochastic environments with unpredictable elements
Solution Approach 1:
The patent segments the trajectory prediction into multiple discrete keypoint predictions rather than modeling the entire continuous trajectory. The model predicts a sequence of keypoints {x1, x2, ..., xT} where each keypoint represents a significant position along the object's path. This segmentation allows the system to handle stochastic environments by making multiple discrete predictions rather than relying on a single deterministic model, thereby improving prediction accuracy while maintaining manageable system complexity.
2Use of energy by moving object
If conventional motion prediction systems are used, then the computational resources are conserved, but the ability to handle non-deterministic environments deteriorates
Solution Approach 1:
The patent employs preliminary action by training the model on synthetic data generated from physics simulations before deployment. The training process pre-computes diverse trajectory scenarios including stochastic elements, allowing the model to learn robust prediction patterns in advance. This preliminary training enables the model to handle non-deterministic environments effectively during inference without requiring excessive computational resources at runtime, as the heavy computational work is performed during the offline training phase.
3Device complexity
If simple trajectory models are used, then the training process is simplified, but the precision of identifying uncertainty regions deteriorates
Solution Approach 1:
The patent implements feedback by using the predicted keypoint sequence to identify uncertainty regions, which then inform the planning of interactions. The system analyzes the predicted trajectory keypoints to determine where uncertainty is high, and uses this information to adjust interaction planning accordingly. This feedback loop allows the relatively simple model structure to achieve precise uncertainty region identification by leveraging the pattern recognition capabilities of the trained model on diverse synthetic data, rather than requiring a more complex model architecture.
Data Source
AI summary
A method for interacting with an object includes identifying, via a keypoint classifier, one or more uncertainty regions in an environment based on an estimated trajectory of an object in the environment. The method also includes planning an interaction with the object based on identifying the one or more uncertainty regions, the planned interaction being within a region of the environment that is different from the one or more uncertainty regions. The method further includes interacting with the object based on planning the interaction.