Safe Whole-Body Loco-Manipulation via Combined Model and Learning-based Control

2026-03-02

AI Summary

This work addresses the challenge of achieving safe and compliant whole-body manipulation for legged robots in dynamic environments by proposing a hybrid architecture that integrates model-driven admittance control with a reinforcement learning–based gait policy. Safety during physical interaction is ensured through a reference governor, while a neural network–enhanced Kalman filter improves base velocity estimation accuracy. A unified whole-body force response is realized using six-degree-of-freedom force/torque sensing. Experimental validation on the Unitree Go2 platform demonstrates significant improvements in high-precision interaction tracking, compliant human–robot collaboration, and safety-critical reliability in dynamic scenarios.

Technology Category

• Intelligent Robots: Manipulation
• Natural Language Processing: Safety and Robustness
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Simultaneous locomotion and manipulation enables robots to interact with their environment beyond the constraints of a fixed base. However, coordinating legged locomotion with arm manipulation, while considering safety and compliance during contact interaction remains challenging. To this end, we propose a whole-body controller that combines a model-based admittance control for the manipulator arm with a Reinforcement Learning (RL) policy for legged locomotion. The admittance controller maps external wrenches--such as those applied by a human during physical interaction--into desired end-effector velocities, allowing for compliant behavior. The velocities are tracked jointly by the arm and leg controllers, enabling a unified 6-DoF force response. The model-based design permits accurate force control and safety guarantees via a Reference Governor (RG), while robustness is further improved by a Kalman filter enhanced with neural networks for reliable base velocity estimation. We validate our approach in both simulation and hardware using the Unitree Go2 quadruped robot with a 6-DoF arm and wrist-mounted 6-DoF Force/Torque sensor. Results demonstrate accurate tracking of interaction-driven velocities, compliant behavior, and safe, reliable performance in dynamic settings.

Problem

• whole-body control
• loco-manipulation
• safety
• compliance
• contact interaction

Innovation

• whole-body control
• admittance control
• reinforcement learning
• Reference Governor
• neural-enhanced Kalman filter