# Adaptive Manipulation using Behavior Trees

arXiv: https://arxiv.org/abs/2406.14634 · HTML: https://mlsanity.com/paper/2406.14634/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 59 references that are in the map.
- First posted on arXiv 20 Jun 2024.

## Builds on (most cited first, 15 of 15)

- [A Survey of Behavior Trees in Robotics and AI](https://mlsanity.com/paper/2005.05842/) (2020)
- [Legs as Manipulator: Pushing Quadrupedal Agility Beyond Locomotion](https://mlsanity.com/paper/2303.11330/) (2023)
- [Towards Blended Reactive Planning and Acting using Behavior Trees](https://mlsanity.com/paper/1611.00230/) (2016)
- [CoSTAR: Instructing Collaborative Robots with Behavior Trees and Vision](https://mlsanity.com/paper/1611.06145/) (2016)
- [Representing Robot Task Plans as Robust Logical-Dynamical Systems](https://mlsanity.com/paper/1908.01896/) (2019)
- [LLM-BT: Performing Robotic Adaptive Tasks based on Large Language Models and Behavior Trees](https://mlsanity.com/paper/2404.05134/) (2024)
- [Behavior Trees and State Machines in Robotics Applications](https://mlsanity.com/paper/2208.04211/) (2022)
- [A Historical Perspective of Adaptive Control and Learning](https://mlsanity.com/paper/2108.11336/) (2021)
- [On the programming effort required to generate Behavior Trees and Finite State Machines for robotic applications](https://mlsanity.com/paper/2209.07392/) (2022)
- [Perceptive Locomotion through Whole-Body MPC and Optimal Region Selection](https://mlsanity.com/paper/2305.08926/) (2023)
- [Interactive Disambiguation for Behavior Tree Execution](https://mlsanity.com/paper/2203.02994/) (2022)
- [A Stack-of-Tasks Approach Combined with Behavior Trees: a New Framework for Robot Control](https://mlsanity.com/paper/2209.08619/) (2022)
- [R-LGP: A Reachability-guided Logic-geometric Programming Framework for Optimal Task and Motion Planning on Mobile Manipulators](https://mlsanity.com/paper/2310.02791/) (2023)
- [CoBT: Collaborative Programming of Behaviour Trees from One Demonstration for Robot Manipulation](https://mlsanity.com/paper/2404.05870/) (2024)
- [Let's Handle It: Generalizable Manipulation of Articulated Objects](https://mlsanity.com/paper/2202.11792/) (2022)

## Built on by (most cited first, 2 of 2)

- [Vision-Language-Policy Model for Dynamic Robot Task Planning](https://mlsanity.com/paper/2512.19178/) (2025)
- [APTree: Extended Behavior Trees for Adaptive and Scalable Automated Robotic Task Planning in Construction](https://mlsanity.com/paper/2610.18523/) (2026)

## Abstract (arXiv)

Many manipulation tasks pose a challenge since they depend on non-visual environmental information that can only be determined after sustained physical interaction has already begun. This is particularly relevant for effort-sensitive, dynamics-dependent tasks such as tightening a valve. To perform these tasks safely and reliably, robots must be able to quickly adapt in response to unexpected changes during task execution, and should also learn from past experience to better inform future decisions. Humans can intuitively respond and adapt their manipulation strategy to suit such problems, but representing and implementing such behaviors for robots remains a challenge. In this work we show how this can be achieved within the framework of behavior trees. We present the adaptive behavior tree, a scalable and generalizable behavior tree design that enables a robot to quickly adapt to and learn from both visual and non-visual observations during task execution, preempting task failure or switching to a different manipulation strategy. The adaptive behavior tree selects the manipulation strategy that is predicted to optimize task performance, and learns from past experience to improve these predictions for future attempts. We test our approach on a variety of tasks commonly found in industry; the adaptive behavior tree demonstrates safety, robustness (100% success rate) and efficiency in task completion (up to 36% task speedup from the baseline).