Robot Navigation Using Gesture-Aware Intermediate Targets
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing low-level navigation policies for robots are not reactive to human gestures and dynamic environmental cues, as they primarily rely on point cloud data and occupancy maps that fail to capture navigational gestures and cues over multiple time steps.
Innovation Solution
A high-level neural network model processes sequences of state data, including images and occupancy maps, to generate position deltas, which are used to provide intermediate target positions and orientations to low-level navigation policies, enabling the robot to respond to dynamic environmental cues without altering the low-level policy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If low-level navigation policies use only point cloud data and occupancy maps, then navigation robustness is improved, but responsiveness to human gestures is lost
Solution Approach 1:
The navigation system is segmented into two independent components: a low-level navigation policy that processes point cloud data and occupancy maps for robust path planning, and a high-level neural network model that processes color images for gesture recognition. Each component maintains its specialized function without interfering with the other's reliability.
Solution Approach 2:
The high-level neural network model acts as an intermediary that translates human gestures captured in color images into navigational cues (such as target position adjustments) that are then fed into the low-level navigation policy. This intermediary layer enables gesture responsiveness without requiring modifications to the robust low-level policy.
2Productivity
If low-level navigation policies process only current state data, then computational efficiency is improved, but ability to recognize multi-step gestures is lost
Solution Approach 1:
The system adds a temporal dimension to gesture recognition by processing sequences of color images through the high-level neural network model. This allows the model to recognize gestures that unfold over multiple time steps while the low-level policy continues to operate efficiently on current state data alone.
Solution Approach 2:
The high-level neural network model performs preliminary processing of color image sequences to extract gesture information and generate navigational cues before passing them to the low-level policy. This preliminary action enables multi-step gesture recognition to occur in advance, allowing the efficient low-level policy to simply follow the pre-processed cues.
3Adaptability or versatility
If high-level neural network model is added to process color images, then gesture recognition capability is improved, but system complexity increases
Solution Approach 1:
The system architecture is segmented into distinct functional modules: the high-level neural network model for gesture recognition, the low-level navigation policy for path planning, and the integration layer that combines their outputs. This segmentation allows each module to be developed, trained, and maintained independently, managing overall system complexity.
Solution Approach 2:
The high-level neural network model serves multiple functions: it processes color images, recognizes various human gestures, extracts navigational cues, and generates target position adjustments. This multi-functionality reduces the need for separate specialized components, thereby managing system complexity while enhancing gesture recognition capability.
Data Source
AI summary
Training and/or utilizing a high-level neural network (NN) model, such as a sequential NN model. The high-level NN model, when trained, can be used to process a sequence of consecutive state data instances (e.g., N most recent, including a current state date instance) to generate a sequence of outputs that indicate a sequence of position deltas. The sequence of position deltas can be used to generate an intermediate target position for navigation and, optionally, an intermediate target orientation that corresponds to the intermediate target position. The intermediate target position and, optionally, the intermediate target orientation, can be provided to a low-level navigation policy, such as an MPC policy, and used by the low-level navigation policy as its goal position (and optionally goal orientation) for a plurality of iterations (e.g., until a new intermediate target position (and optionally new target orientation) is generated using the high-level NN model.