mlsanityData to 7 Oct 2026

Tuning Legged Locomotion Controllers via Safe Bayesian Optimization

arXiv 2306.07092 ↗First posted 12 Jun 2023cs.RO cs.AIcited by 8

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Abstract (arXiv)

This paper presents a data-driven strategy to streamline the deployment of model-based controllers in legged robotic hardware platforms. Our approach leverages a model-free safe learning algorithm to automate the tuning of control gains, addressing the mismatch between the simplified model used in the control formulation and the real system. This method substantially mitigates the risk of hazardous interactions with the robot by sample-efficiently optimizing parameters within a probably safe region. Additionally, we extend the applicability of our approach to incorporate the different gait parameters as contexts, leading to a safe, sample-efficient exploration algorithm capable of tuning a motion controller for diverse gait patterns. We validate our method through simulation and hardware experiments, where we demonstrate that the algorithm obtains superior performance on tuning a model-based motion controller for multiple gaits safely.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 21 of its 40 references that are in the map. First posted on arXiv 12 Jun 2023.

Builds on

21 of 40 references.

References · 21

  1. 2016OpenAI Gymcited by 1,684
  2. 2019Learning agile and dynamic motor skills for legged robotscited by 402
  3. 2017On Kernelized Multi-armed Banditscited by 348
  4. 2018Sim-to-Real: Learning Agile Locomotion For Quadruped Robotscited by 277
  5. 2020On Information Gain and Regret Bounds in Gaussian Process Banditscited by 156
  6. 2016Bayesian Optimization with Safety Constraints: Safe and Automatic Parameter Tuning in Roboticscited by 134
  7. 2014Bayesian Optimization with Unknown Constraintscited by 109
  8. 2018Stagewise Safe Bayesian Optimization with Gaussian Processescited by 79
  9. 2015A General Framework for Constrained Bayesian Optimization using Information-based Searchcited by 50
  10. 2019No-Regret Bayesian Optimization with Unknown Hyperparameterscited by 50
  11. 2021Practical and Rigorous Uncertainty Bounds for Gaussian Process Regressioncited by 49
  12. 2018Constant-Time Predictive Distributions for Gaussian Processescited by 48
9 more
  1. 2019Highly Dynamic Quadruped Locomotion via Whole-Body Impulse Control and Model Predictive Controlcited by 43
  2. 2021GoSafe: Globally Optimal Safe Robot Learningcited by 27
  3. 2019Safe Contextual Bayesian Optimization for Sustainable Room Temperature PID Control Tuningcited by 24
  4. 2022GoSafeOpt: Scalable Safe Exploration for Global Optimization of Dynamical Systemscited by 17
  5. 2020Robot Learning with Crash Constraintscited by 15
  6. 2022Meta-Learning Priors for Safe Bayesian Optimizationcited by 14
  7. 2023RL + Model-based Control: Using On-demand Optimal Control to Learn Versatile Legged Locomotioncited by 14
  8. 2021Safe and Efficient Model-free Adaptive Control via Bayesian Optimizationcited by 10
  9. 2022Nonlinear Model Predictive Control for Quadrupedal Locomotion Using Second-Order Sensitivity Analysiscited by 2

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 5; 2025: 2.

8 papers.

  1. 2024ActSafe: Active Exploration with Safety Constraints for Reinforcement Learningcited by 22
  2. 2023Bayesian Optimization with Formal Safety Guarantees via Online Conformal Predictioncited by 10
  3. 2024Bridging the Sim-to-Real Gap with Bayesian Inferencecited by 6
  4. 2024Safe Exploration Using Bayesian World Models and Log-Barrier Optimizationcited by 4
  5. 2024Safe Time-Varying Optimization based on Gaussian Processes with Spatio-Temporal Kernelcited by 4
  6. 2024Safe Bayesian Optimization for the Control of High-Dimensional Embodied Systemscited by 4
  7. 2025Simulation Priors for Data-Efficient Deep Learningcited by 0
  8. 2025Bayesian Optimization for Automatic Tuning of Torque-Level Nonlinear Model Predictive Controlcited by 0