Learning of assembly constraints by demonstration and active exploration

Purpose – In this paper we propose a method for learning robotic assembly sequences, where precedence constraints and object relative size and location constraints can be learned by demonstration and autonomous robot exploration. Design/methodology/approach – In order to successfully plan the operations involved in assembly tasks, the planner needs to know the constraints of the desired task. In this paper we propose a methodology for learning such constraints by demonstration and autonomous exploration. We investigated the learning of precedence constraints and object relative size and location constraints, which are needed to construct a planner for automated assembly. In the developed system, the learning of symbolic constraints is integrated with low–level control algorithms, which is essential to enable active robot learning. Findings – We demonstrated that the proposed reasoning algorithms can be used to learn previously unknown assembly constraints that are needed to implement a planner for automated assembly. Cranfield benchmark, which is a standardized benchmark for testing algorithms for robot assembly, was used to evaluate the proposed approaches. We evaluated the learning performance both in simulation and on a real robot. Practical implications (if applicable) – Our approach reduces the amount of programming that is needed to set up new assembly cells and consequently the overall set up time when new products are introduced into the workcell. Originality/value – In this paper we propose a new approach for learning assembly constraints based on programming by demonstration and active robot exploration to reduce the computational complexity of the underlying search problems. We developed algorithms for success/failure detection of assembly operations based on the comparison of expected signals (forces and torques, positions and orientations of the assembly parts) with the actual signals sensed by a robot. In this manner all precedence and object size and location constraints can be learned, thereby providing the necessary input for the optimal planning of the entire assembly process.