# Goal-Driven Dynamics Learning via Bayesian Optimization

arXiv: https://arxiv.org/abs/1703.09260 · HTML: https://mlsanity.com/paper/1703.09260/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2017.

## Built on by (most cited first, 19 of 19)

- [Investigating Compounding Prediction Errors in Learned Dynamics Models](https://mlsanity.com/paper/2203.09637/) (2022)
- [Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coverage](https://mlsanity.com/paper/2106.03207/) (2021)
- [Control-Aware Prediction Objectives for Autonomous Driving](https://mlsanity.com/paper/2204.13319/) (2022)
- [PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided Exploration](https://mlsanity.com/paper/2107.07410/) (2021)
- [TaskMet: Task-Driven Metric Learning for Model Learning](https://mlsanity.com/paper/2312.05250/) (2023)
- [VABO: Violation-Aware Bayesian Optimization for Closed-Loop Control Performance Optimization with Unmodeled Constraints](https://mlsanity.com/paper/2110.07479/) (2021)
- [A Unified View on Solving Objective Mismatch in Model-Based Reinforcement Learning](https://mlsanity.com/paper/2310.06253/) (2023)
- [Enhancing Gaussian Process Surrogates for Optimization and Posterior Approximation via Random Exploration](https://mlsanity.com/paper/2401.17037/) (2024)
- [Optimization of the Model Predictive Control Meta-Parameters Through Reinforcement Learning](https://mlsanity.com/paper/2111.04146/) (2021)
- [Lower Bounds on the Worst-Case Complexity of Efficient Global Optimization](https://mlsanity.com/paper/2209.09655/) (2022)
- [Violation-Aware Contextual Bayesian Optimization for Controller Performance Optimization with Unmodeled Constraints](https://mlsanity.com/paper/2301.12099/) (2023)
- [Optimizing Closed-Loop Performance with Data from Similar Systems: A Bayesian Meta-Learning Approach](https://mlsanity.com/paper/2211.00077/) (2022)
- [Risk-Driven Design of Perception Systems](https://mlsanity.com/paper/2205.10677/) (2022)
- [AVOIDDS: Aircraft Vision-based Intruder Detection Dataset and Simulator](https://mlsanity.com/paper/2306.11203/) (2023)
- [Regret Bounds for Noise-Free Cascaded Kernelized Bandits](https://mlsanity.com/paper/2211.05430/) (2022)
- [CONFIG: Constrained Efficient Global Optimization for Closed-Loop Control System Optimization with Unmodeled Constraints](https://mlsanity.com/paper/2211.11822/) (2022)
- [Performance-Driven Controller Tuning via Derivative-Free Reinforcement Learning](https://mlsanity.com/paper/2209.04854/) (2022)
- [Incorporating Recurrent Reinforcement Learning into Model Predictive Control for Adaptive Control in Autonomous Driving](https://mlsanity.com/paper/2301.13313/) (2023)
- [Efficient Automatic Tuning for Data-driven Model Predictive Control via Meta-Learning](https://mlsanity.com/paper/2404.00232/) (2024)

## Abstract (arXiv)

Real-world robots are becoming increasingly complex and commonly act in poorly understood environments where it is extremely challenging to model or learn their true dynamics. Therefore, it might be desirable to take a task-specific approach, wherein the focus is on explicitly learning the dynamics model which achieves the best control performance for the task at hand, rather than learning the true dynamics. In this work, we use Bayesian optimization in an active learning framework where a locally linear dynamics model is learned with the intent of maximizing the control performance, and used in conjunction with optimal control schemes to efficiently design a controller for a given task. This model is updated directly based on the performance observed in experiments on the physical system in an iterative manner until a desired performance is achieved. We demonstrate the efficacy of the proposed approach through simulations and real experiments on a quadrotor testbed.