# Adaptable Recovery Behaviors in Robotics: A Behavior Trees and Motion Generators(BTMG) Approach for Failure Management

arXiv: https://arxiv.org/abs/2404.06129 · HTML: https://mlsanity.com/paper/2404.06129/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 38 references that are in the map.
- First posted on arXiv 9 Apr 2024.

## Builds on (most cited first, 25 of 25)

- [Behavior Trees in Robotics and AI: An Introduction](https://mlsanity.com/paper/1709.00084/) (2017)
- [Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation Tasks](https://mlsanity.com/paper/2110.03655/) (2021)
- [A Survey of Behavior Trees in Robotics and AI](https://mlsanity.com/paper/2005.05842/) (2020)
- [A survey on policy search algorithms for learning robot controllers in a handful of trials](https://mlsanity.com/paper/1807.02303/) (2018)
- [Practical Design Space Exploration](https://mlsanity.com/paper/1810.05236/) (2018)
- [Robust Recovery Controller for a Quadrupedal Robot using Deep Reinforcement Learning](https://mlsanity.com/paper/1901.07517/) (2019)
- [Active Inference and Behavior Trees for Reactive Action Planning and Execution in Robotics](https://mlsanity.com/paper/2011.09756/) (2020)
- [Representing Robot Task Plans as Robust Logical-Dynamical Systems](https://mlsanity.com/paper/1908.01896/) (2019)
- [Black-Box Data-efficient Policy Search for Robotics](https://mlsanity.com/paper/1703.07261/) (2017)
- [Behavior Trees and State Machines in Robotics Applications](https://mlsanity.com/paper/2208.04211/) (2022)
- [On modularity in reactive control architectures, with an application to formal verification](https://mlsanity.com/paper/2008.12515/) (2020)
- [Skill-based Multi-objective Reinforcement Learning of Industrial Robot Tasks with Planning and Knowledge Integration](https://mlsanity.com/paper/2203.10033/) (2022)
- [Learning Skill-based Industrial Robot Tasks with User Priors](https://mlsanity.com/paper/2208.01605/) (2022)
- [BeBOP -- Combining Reactive Planning and Bayesian Optimization to Solve Robotic Manipulation Tasks](https://mlsanity.com/paper/2310.00971/) (2023)
- [On the programming effort required to generate Behavior Trees and Finite State Machines for robotic applications](https://mlsanity.com/paper/2209.07392/) (2022)
- [Learning to Adapt the Parameters of Behavior Trees and Motion Generators (BTMGs) to Task Variations](https://mlsanity.com/paper/2303.08209/) (2023)
- [Learning of Parameters in Behavior Trees for Movement Skills](https://mlsanity.com/paper/2109.13050/) (2021)
- [Combining Planning, Reasoning and Reinforcement Learning to solve Industrial Robot Tasks](https://mlsanity.com/paper/2212.03570/) (2022)
- [SkiROS2: A skill-based Robot Control Platform for ROS](https://mlsanity.com/paper/2306.17030/) (2023)
- [Fast Damage Recovery in Robotics with the T-Resilience Algorithm](https://mlsanity.com/paper/1302.0386/) (2013)
- [Secure Recovery Procedure for Manufacturing Systems using Synchronizing Automata and Supervisory Control Theory](https://mlsanity.com/paper/2008.13062/) (2020)
- [Reactive Locomotion Decision-Making and Robust Motion Planning for Real-Time Perturbation Recovery](https://mlsanity.com/paper/2110.03037/) (2021)
- [A C++ Implementation of a Cartesian Impedance Controller for Robotic Manipulators](https://mlsanity.com/paper/2212.11215/) (2022)
- [Recovery of Behaviors Encoded via Bilateral Constraints](https://mlsanity.com/paper/2005.00506/) (2020)
- [Using Knowledge Representation and Task Planning for Robot-agnostic Skills on the Example of Contact-Rich Wiping Tasks](https://mlsanity.com/paper/2308.14206/) (2023)

## Built on by (most cited first, 1 of 1)

- [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)

## Abstract (arXiv)

In dynamic operational environments, particularly in collaborative robotics, the inevitability of failures necessitates robust and adaptable recovery strategies. Traditional automated recovery strategies, while effective for predefined scenarios, often lack the flexibility required for on-the-fly task management and adaptation to expected failures. Addressing this gap, we propose a novel approach that models recovery behaviors as adaptable robotic skills, leveraging the Behavior Trees and Motion Generators~(BTMG) framework for policy representation. This approach distinguishes itself by employing reinforcement learning~(RL) to dynamically refine recovery behavior parameters, enabling a tailored response to a wide array of failure scenarios with minimal human intervention. We assess our methodology through a series of progressively challenging scenarios within a peg-in-a-hole task, demonstrating the approach's effectiveness in enhancing operational efficiency and task success rates in collaborative robotics settings. We validate our approach using a dual-arm KUKA robot.