UT Shield

Manipulation

, Filed Under: Manipulation, Nuclear, Research

Affordance Templates for Complex DOE Robotics Tasks

A screw-based affordance template framework enables adaptive robotic execution of complex contact tasks (CCTs) in DOE decontamination and decommissioning (D&D) environments.

By modeling tasks with screw and wrench primitives, the approach supports real-time reconfiguration, force-adaptive control, and scalable task generalization. A Pre-Planner module refines motion plans by integrating collision constraints and dynamically adjusting trajectories based on real-time Force/Torque (F/T) feedback.

The framework has been validated at the NRG lab and the Hanford site, demonstrating scalability, robustness, and generalization across robotic platforms for challenging nuclear maintenance tasks.

, Filed Under: Manipulation, Nuclear, Research, Space

Closed-Chain Affordance Planner

Robots operating in unpredictable environments require versatile, hardware-agnostic frameworks capable of adapting to various tasks. While a recent screw-based affordance approach shows promise, it faces challenges in avoiding undesirable configurations, singularity navigation, and task success prediction. To address these limitations, we propose the Closed-Chain Affordance (CCA) planner – a novel framework that incorporates explicit gripper orientation control and generates complete joint trajectories in real time for screw-based task affordance execution. Our method models the affordance and manipulator as a closed-chain mechanism, introducing an innovative approach to solving closed-chain inverse kinematics. It encapsulates task constraints and simplifies task definitions, while remaining hardware and robot agnostic, robust to errors, and invariant to the initial grasp. We validate our framework with simulations on a UR5 robot and real-world implementation on a Boston Dynamics Spot robot. Our experiments demonstrate rapid joint trajectory generation (0.0077–0.098 s) for various tasks, including a 420° valve turn with gripper orientation control. Comparison with state-of-the-art methods shows a 4x improvement in planning time, reduced joint movement, and achievement of greater task goals.