Mobile manipulators optimize force for better task performance
FORTE: Task-Adaptive Force Capability Optimization for Mobile Manipulators
Robotics
Summary
Robots that move around and handle objects need to balance how much force they can apply with how easily they can move. The authors created a way for robots to adjust their force capability based on the task's needs, using images and descriptions to predict forces required. Their method helps the robot use just enough force for each job, keeping it flexible and efficient. Tests show this approach works better than fixed-force or motion-only methods.
What this means in practice
- •For robotic system integrators: Configure mobile manipulators to optimize force and dexterity based on specific physical interaction tasks.
- •For industrial automation teams: Increase efficiency in robotic lifting and holding tasks by dynamically balancing force use and flexibility in mobile robots.
Authors
Xiao Wang, Heng Zhang, Gokhan Solak, Fei Zhao, Arash Ajoudani
Abstract
Effective physical interaction control in robotic manipulation requires not only kinematically feasible motion but also sufficient force-interaction capability. Existing redundancy resolution methods often ignore task-specific force demands or maximize the force capability indiscriminately, sacrificing dexterity when large force margins are unnecessary. We propose a task-oriented force capability optimization framework for redundant mobile manipulators. A Vision-Language Model (VLM) infers object physical properties from an RGB image and a task description, generating a desired task-force sequence that captures gravitational and inertial demands. We then define a task-oriented force capability metric as the signed distance between a task-force uncertainty ball and the dynamic residual force polytope (RFP), quantifying compatibility between task demands and the robot's remaining actuation capacity. This metric is incorporated, alongside manipulability, joint-limit avoidance, trajectory smoothness, and base-oscillation suppression, into a whole-body multi-objective trajectory-optimization problem. Experiments on a mobile manipulator performing lifting and single-point-holding tasks under varying payload conditions demonstrate that the proposed method provides sufficient force capability for heavy loads while preserving high manipulability for light loads. This yields a task-adaptive balance that fixed capability-maximizing baselines (RFP inscribed radius, RFP cone) and manipulability-only optimization fail to achieve. The core implementation is publicly available at https://github.com/yeying256/FORTE.