# BeBOP -- Combining Reactive Planning and Bayesian Optimization to Solve Robotic Manipulation Tasks

arXiv: https://arxiv.org/abs/2310.00971 · HTML: https://mlsanity.com/paper/2310.00971/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2023.

## Built on by (most cited first, 6 of 6)

- [Action Tokenizer Matters in In-Context Imitation Learning](https://mlsanity.com/paper/2503.01206/) (2025)
- [A Unified Framework for Real-Time Failure Handling in Robotics Using Vision-Language Models, Reactive Planner and Behavior Trees](https://mlsanity.com/paper/2503.15202/) (2025)
- [Adaptable Recovery Behaviors in Robotics: A Behavior Trees and Motion Generators(BTMG) Approach for Failure Management](https://mlsanity.com/paper/2404.06129/) (2024)
- [On the Fly Adaptation of Behavior Tree-Based Policies through Reinforcement Learning](https://mlsanity.com/paper/2503.06309/) (2025)
- [A Hierarchical Spatiotemporal Action Tokenizer for In-Context Imitation Learning in Robotics](https://mlsanity.com/paper/2604.15215/) (2026)
- [Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery](https://mlsanity.com/paper/2607.29225/) (2026)

## Abstract (arXiv)

Robotic systems for manipulation tasks are increasingly expected to be easy to configure for new tasks. While in the past, robot programs were often written statically and tuned manually, the current, faster transition times call for robust, modular and interpretable solutions that also allow a robotic system to learn how to perform a task. We propose the method Behavior-based Bayesian Optimization and Planning (BeBOP) that combines two approaches for generating behavior trees: we build the structure using a reactive planner and learn specific parameters with Bayesian optimization. The method is evaluated on a set of robotic manipulation benchmarks and is shown to outperform state-of-the-art reinforcement learning algorithms by being up to 46 times faster while simultaneously being less dependent on reward shaping. We also propose a modification to the uncertainty estimate for the random forest surrogate models that drastically improves the results.