# Skill-based Multi-objective Reinforcement Learning of Industrial Robot Tasks with Planning and Knowledge Integration

arXiv: https://arxiv.org/abs/2203.10033 · HTML: https://mlsanity.com/paper/2203.10033/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 45 references that are in the map.
- First posted on arXiv 18 Mar 2022.

## Builds on (most cited first, 17 of 17)

- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [Behavior Trees in Robotics and AI: An Introduction](https://mlsanity.com/paper/1709.00084/) (2017)
- [A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations](https://mlsanity.com/paper/1805.12168/) (2018)
- [Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks](https://mlsanity.com/paper/1906.08880/) (2019)
- [PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making](https://mlsanity.com/paper/1804.07779/) (2018)
- [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)
- [SPOTTER: Extending Symbolic Planning Operators through Targeted Reinforcement Learning](https://mlsanity.com/paper/2012.13037/) (2020)
- [CoSTAR: Instructing Collaborative Robots with Behavior Trees and Vision](https://mlsanity.com/paper/1611.06145/) (2016)
- [A Comparison of Action Spaces for Learning Manipulation Tasks](https://mlsanity.com/paper/1908.08659/) (2019)
- [Black-Box Data-efficient Policy Search for Robotics](https://mlsanity.com/paper/1703.07261/) (2017)
- [What Should I Do Now? Marrying Reinforcement Learning and Symbolic Planning](https://mlsanity.com/paper/1901.01492/) (2019)
- [Combining Planning and Learning of Behavior Trees for Robotic Assembly](https://mlsanity.com/paper/2103.09036/) (2021)
- [Multi-objective Model-based Policy Search for Data-efficient Learning with Sparse Rewards](https://mlsanity.com/paper/1806.09351/) (2018)
- [Algorithmic Performance-Accuracy Trade-off in 3D Vision Applications Using HyperMapper](https://mlsanity.com/paper/1702.00505/) (2017)
- [Learning of Parameters in Behavior Trees for Movement Skills](https://mlsanity.com/paper/2109.13050/) (2021)
- [A New Skill Based Robot Programming Language Using UML/P Statecharts](https://mlsanity.com/paper/1409.6601/) (2014)

## Built on by (most cited first, 10 of 10)

- [Joint Entropy Search for Maximally-Informed Bayesian Optimization](https://mlsanity.com/paper/2206.04771/) (2022)
- [Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces](https://mlsanity.com/paper/2304.11468/) (2023)
- [Bounce: Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed Spaces](https://mlsanity.com/paper/2307.00618/) (2023)
- [Self-Correcting Bayesian Optimization through Bayesian Active Learning](https://mlsanity.com/paper/2304.11005/) (2023)
- [Understanding High-Dimensional Bayesian Optimization](https://mlsanity.com/paper/2502.09198/) (2025)
- [SkiROS2: A skill-based Robot Control Platform for ROS](https://mlsanity.com/paper/2306.17030/) (2023)
- [Falsification of Cyber-Physical Systems using Bayesian Optimization](https://mlsanity.com/paper/2209.06735/) (2022)
- [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)
- [Adaptable Recovery Behaviors in Robotics: A Behavior Trees and Motion Generators(BTMG) Approach for Failure Management](https://mlsanity.com/paper/2404.06129/) (2024)
- [Shadow Program Inversion with Differentiable Planning: A Framework for Unified Robot Program Parameter and Trajectory Optimization](https://mlsanity.com/paper/2409.08678/) (2024)

## Abstract (arXiv)

In modern industrial settings with small batch sizes it should be easy to set up a robot system for a new task. Strategies exist, e.g. the use of skills, but when it comes to handling forces and torques, these systems often fall short. We introduce an approach that provides a combination of task-level planning with targeted learning of scenario-specific parameters for skill-based systems. We propose the following pipeline: (1) the user provides a task goal in the planning language PDDL, (2) a plan (i.e., a sequence of skills) is generated and the learnable parameters of the skills are automatically identified. An operator then chooses (3) reward functions and hyperparameters for the learning process. Two aspects of our methodology are critical: (a) learning is tightly integrated with a knowledge framework to support symbolic planning and to provide priors for learning, (b) using multi-objective optimization. This can help to balance key performance indicators (KPIs) such as safety and task performance since they can often affect each other. We adopt a multi-objective Bayesian optimization approach and learn entirely in simulation. We demonstrate the efficacy and versatility of our approach by learning skill parameters for two different contact-rich tasks. We show their successful execution on a real 7-DOF KUKA-iiwa manipulator and outperform the manual parameterization by human robot operators.