RAMBO: RL-Augmented Model-Based Whole-Body Control for Loco-Manipulation
Jin Cheng, Dongho Kang, Gabriele Fadini, Guanya Shi, Stelian Coros
AI summary
Problem
Legged robots struggle to balance the need for precise end-effector force control, typically provided by model-based methods, with the robustness to unmodeled dynamics offered by learning-based methods.
Approach
A hybrid framework that uses a model-based whole-body controller to generate feedforward torques via quadratic programming, augmented by an RL policy that provides corrective feedback for base acceleration and joint positions.
Key results
- Successful real-world tasks including pushing a shopping cart, balancing a plate, and holding soft objects
- Demonstrated capability in both quadrupedal and bipedal walking modes
- Precise end-effector tracking while maintaining robust dynamic locomotion
- Validated on the Unitree Go2 quadruped platform
Why it matters
This framework enables legged robots to perform complex physical interactions with their environment without sacrificing stability or precision.
Abstract
Loco-manipulation, physical interaction of vari- ous objects that is concurrently coordinated with locomotion, remains a major challenge for legged robots due to the need for both precise end-effector control and robustness to unmodeled dynamics. While model-based controllers provide precise planning via online optimization, they are limited by model inaccuracies. In contrast, learning-based methods offer robustness, but they struggle with precise modulation of inter- action forces. We introduce RAMBO, a hybrid framework that integrates model-based whole-body control within a feedback policy trained with reinforcement learning. The model-based module generates feedforward torques by solving a quadratic program, while the policy provides feedback corrective terms to enhance robustness. We validate our framework on a quadruped robot across a diverse set of real-world loco- manipulation tasks, such as pushing a shopping cart, balancing a plate, and holding soft objects, in both quadrupedal and bipedal walking. Our experiments demonstrate that RAMBO enables precise manipulation capabilities while achieving robust and dynamic locomotion.