# Autotuning Bipedal Locomotion MPC with GRFM-Net for Efficient Sim-to-Real Transfer

arXiv: https://arxiv.org/abs/2409.15710 · HTML: https://mlsanity.com/paper/2409.15710/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 51 references that are in the map.
- First posted on arXiv 24 Sep 2024.

## Builds on (most cited first, 28 of 28)

- [Learning agile and dynamic motor skills for legged robots](https://mlsanity.com/paper/1901.08652/) (2019)
- [Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation](https://mlsanity.com/paper/2106.13281/) (2021)
- [DiffTaichi: Differentiable Programming for Physical Simulation](https://mlsanity.com/paper/1910.00935/) (2019)
- [Accelerated Policy Learning with Parallel Differentiable Simulation](https://mlsanity.com/paper/2204.07137/) (2022)
- [Do Differentiable Simulators Give Better Policy Gradients?](https://mlsanity.com/paper/2202.00817/) (2022)
- [Fast and Feature-Complete Differentiable Physics for Articulated Rigid Bodies with Contact](https://mlsanity.com/paper/2103.16021/) (2021)
- [Optimization-Based Control for Dynamic Legged Robots](https://mlsanity.com/paper/2211.11644/) (2022)
- [A Review of Differentiable Simulators](https://mlsanity.com/paper/2407.05560/) (2024)
- [Bayesian Optimization Using Domain Knowledge on the ATRIAS Biped](https://mlsanity.com/paper/1709.06047/) (2017)
- [The Curse of Unrolling: Rate of Differentiating Through Optimization](https://mlsanity.com/paper/2209.13271/) (2022)
- [Tailoring Solution Accuracy for Fast Whole-body Model Predictive Control of Legged Robots](https://mlsanity.com/paper/2407.10789/) (2024)
- [Dynamic Locomotion For Passive-Ankle Biped Robots And Humanoids Using Whole-Body Locomotion Control](https://mlsanity.com/paper/1901.08100/) (2019)
- [Integrated Task and Motion Planning for Safe Legged Navigation in Partially Observable Environments](https://mlsanity.com/paper/2110.12097/) (2021)
- [Recursive Feasibility Guided Optimal Parameter Adaptation of Differential Convex Optimization Policies for Safety-Critical Systems](https://mlsanity.com/paper/2109.10949/) (2021)
- [Stability of Surface Contacts for Humanoid Robots: Closed-Form Formulae of the Contact Wrench Cone for Rectangular Support Areas](https://mlsanity.com/paper/1501.04719/) (2015)
- [Walking-by-Logic: Signal Temporal Logic-Guided Model Predictive Control for Bipedal Locomotion Resilient to External Perturbations](https://mlsanity.com/paper/2309.13172/) (2023)
- [DiffLoop: Tuning PID controllers by differentiating through the feedback loop](https://mlsanity.com/paper/2106.10516/) (2021)
- [Autonomous Navigation of Underactuated Bipedal Robots in Height-Constrained Environments](https://mlsanity.com/paper/2109.05714/) (2021)
- [Bayesian Optimization Meets Hybrid Zero Dynamics: Safe Parameter Learning for Bipedal Locomotion Control](https://mlsanity.com/paper/2203.02570/) (2022)
- [Dynamic Loco-manipulation on HECTOR: Humanoid for Enhanced ConTrol and Open-source Research](https://mlsanity.com/paper/2312.11868/) (2023)
- [LQR-Assisted Whole-Body Control of a Wheeled Bipedal Robot with Kinematic Loops](https://mlsanity.com/paper/2005.11431/) (2020)
- [Robust walking based on MPC with viability guarantees](https://mlsanity.com/paper/2010.04514/) (2020)
- [Global Position Control on Underactuated Bipedal Robots: Step-to-step Dynamics Approximation for Step Planning](https://mlsanity.com/paper/2011.06050/) (2020)
- [Force-and-moment-based Model Predictive Control for Achieving Highly Dynamic Locomotion on Bipedal Robots](https://mlsanity.com/paper/2104.00065/) (2021)
- [Planar Bipedal Locomotion with Nonlinear Model Predictive Control: Online Gait Generation using Whole-Body Dynamics](https://mlsanity.com/paper/2203.07429/) (2022)
- [DiffTune-MPC: Closed-Loop Learning for Model Predictive Control](https://mlsanity.com/paper/2312.11384/) (2023)
- [DiffTune^+: Hyperparameter-Free Auto-Tuning using Auto-Differentiation](https://mlsanity.com/paper/2212.03194/) (2022)
- [Gaussian Process-Based Learning Control of Underactuated Balance Robots with an External and Internal Convertible Modeling Structure](https://mlsanity.com/paper/2312.10155/) (2023)

## Abstract (arXiv)

Bipedal locomotion control is essential for humanoid robots to navigate complex, human-centric environments. While optimization-based control designs are popular for integrating sophisticated models of humanoid robots, they often require labor-intensive manual tuning. In this work, we address the challenges of parameter selection in bipedal locomotion control using DiffTune, a model-based autotuning method that leverages differential programming for efficient parameter learning. A major difficulty lies in balancing model fidelity with differentiability. We address this difficulty using a low-fidelity model for differentiability, enhanced by a Ground Reaction Force-and-Moment Network (GRFM-Net) to capture discrepancies between MPC commands and actual control effects. We validate the parameters learned by DiffTune with GRFM-Net in hardware experiments, which demonstrates the parameters' optimality in a multi-objective setting compared with baseline parameters, reducing the total loss by up to 40.5% compared with the expert-tuned parameters. The results confirm the GRFM-Net's effectiveness in mitigating the sim-to-real gap, improving the transferability of simulation-learned parameters to real hardware.