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Found 5 papers, 2 papers with code
Adaptable Recovery Behaviors in Robotics: A Behavior Trees and Motion Generators(BTMG) Approach for Failure Management
In dynamic operational environments, particularly in collaborative robotics, the inevitability of failures necessitates robust and adaptable recovery strategies.
Using Knowledge Representation and Task Planning for Robot-agnostic Skills on the Example of Contact-Rich Wiping Tasks
The transition to agile manufacturing, Industry 4. 0, and high-mix-low-volume tasks require robot programming solutions that are flexible.
Skill-based Multi-objective Reinforcement Learning of Industrial Robot Tasks with Planning and Knowledge Integration
We introduce an approach that provides a combination of task-level planning with targeted learning of scenario-specific parameters for skill-based systems.
Bayesian Optimization
Multi-Objective Reinforcement Learning
Learning of Parameters in Behavior Trees for Movement Skills
Reinforcement Learning (RL) is a powerful mathematical framework that allows robots to learn complex skills by trial-and-error.
A Formal Framework for Robot Construction Problems: A Hybrid Planning Approach
This framework not only decides for a stable final configuration of the structure, but also computes the order of manipulation tasks for multiple autonomous robots to build the structure from an initial configuration, while simultaneously ensuring the stability, supportedness and other desired properties of the partial construction at each step of the plan.
