Safe Whole-Body Loco-Manipulation Via Combined Model and Learning-Based Control
Alexander Schperberg, Yeping Wang, Stefano Di Cairano
AI summary
Problem
Coordinating legged locomotion with arm manipulation while ensuring safety, compliance, and accurate force tracking during physical interaction remains challenging due to complex coupled dynamics and the lack of formal safety guarantees in pure learning-based methods.
Approach
The method combines a model-based admittance controller for the manipulator arm with a reinforcement learning policy for legged locomotion, augmented by a neural-enhanced Kalman filter for robust state estimation and a Reference Governor to enforce safety constraints.
Key results
- Explicit force and torque tracking via wrist-mounted F/T sensors
- Formal safety guarantees for the manipulator arm using a Reference Governor
- Neural network-enhanced Kalman filter for robust base velocity estimation
- Validated compliant and safe loco-manipulation in simulation and hardware
Why it matters
Provides a reliable framework for safe human-robot collaboration and precise force-controlled manipulation on legged mobile manipulators.
Abstract
Simultaneous locomotion and manipulation en- ables 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.