Robot Motion Retargeting for Force-Consistent Human Interaction
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Solution Overview
Problem
Current systems for telepresence robots lack the capability for efficient direct physical interaction with humans, failing to replicate the emotional and physical aspects of in-person communication effectively.
Innovation Solution
A control system for robots that includes an admittance controller, retargeting controller, force optimization controller, motion optimization controller, and motor controller, which calculates desired velocities and trajectories to map human contact states to robot contact states, optimizing force and motion to simulate human-like interaction through transformation matrices and joint commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a robot system uses basic teleoperation control, then the system structure is simple, but it cannot achieve realistic physical interaction with humans
Solution Approach 1:
The control system is divided into multiple specialized controllers: admittance controller for force-based velocity calculation, retargeting controller for motion mapping, force optimization controller for contact force optimization, motion optimization controller for trajectory generation, and tracking controller for pose tracking. Each controller handles a specific aspect of the interaction, enabling realistic physical interaction while managing overall system complexity through functional decomposition.
Solution Approach 2:
The retargeting controller acts as an intermediary that maps human contact states to robot contact states using transformation matrices. This intermediary layer translates human motion and force intentions into robot-compatible commands, enabling natural physical interaction without requiring direct one-to-one control mapping.
2Force
If the robot prioritizes force consistency in physical interaction, then interaction realism is improved, but tangential motion control becomes more difficult
Solution Approach 1:
The force optimization controller applies different optimization strategies to different aspects of contact: normal forces are optimized for consistency with human input, while tangential motions are optimized separately to minimize unnecessary movement. This local differentiation allows force consistency to be prioritized where needed without compromising tangential motion control.
Solution Approach 2:
The system changes optimization parameters based on contact conditions. The force optimization controller adjusts the weighting between force consistency and motion smoothness dynamically, allowing the system to maintain force fidelity while adapting tangential motion control to the specific interaction context.
Data Source
AI summary
Controlling a robot may be performed by calculating a desired velocity based on an operator contact force and a robot contact force, calculating a transformation matrix based on an operator pose, a robot pose, a robot trajectory, the operator contact force, and the robot contact force, calculating a least square solution to Jf+{circumflex over (v)}f based on the desired velocity, calculating a scaling factor based on a current joint position of a joint of the robot, calculating a first trajectory based on the least square solution, the scaling factor, and the transformation matrix, calculating the robot trajectory, calculating a joint command based on the robot trajectory and the robot pose, and implementing the joint command.