Abstract
In manufacturing, traditional task pre-programming methods limit the efficiency of human–robot skill transfer. This paper proposes a novel task-learning strategy, enabling robots to learn skills from human demonstrations flexibly and generalize skills under new task situations. Specifically, we establish a markerless vision capture system to acquire continuous human hand movements and develop a threshold-based heuristic segmentation algorithm to segment the complete movements into different movement primitives (MPs) which encode human hand movements with task-oriented models. For movement primitive learning, we adopt a Gaussian mixture model and Gaussian mixture regression (GMM-GMR) to extract the optimal trajectory encapsulating sufficient human features and utilize dynamical movement primitives (DMPs) to learn for trajectory generalization. In addition, we propose an improved visuo-spatial skill learning (VSL) algorithm to learn goal configurations concerning spatial relationships between task-relevant objects. Only one multioperation demonstration is required for learning, and robots can generalize goal configurations under new task situations following the task execution order from demonstration. A series of peg-in-hole experiments demonstrate that the proposed task-learning strategy can obtain exact pick-and-place points and generate smooth human-like trajectories, verifying the effectiveness of the proposed strategy.
Highlights
Recent advances in artificial intelligence and sensor technology have heightened the need for robots to perform assembly tasks autonomously
Inspired by research on vision-based action recognition [33] that possible Grasp and Release points may occur at the local lowest points of human hand palm motions, we propose a heuristic segmentation algorithm based on human hand centroid velocities, as depicted in Algorithm 1
We focus on discrete movements and encode each degree of freedom (DOF) in Cartesian space with a separated dynamical movement primitives (DMPs) described by the canonical system: τ ẋ = −α x x and the transformation system: τ ż = αz ( β z ( G − y) − z) + f τ ẏ = z, (9)
Summary
Recent advances in artificial intelligence and sensor technology have heightened the need for robots to perform assembly tasks autonomously. The applications in manufacturing remain a significant challenge, since traditional industrial robots deployed in production lines are pre-programmed for a specific task in a carefully structured environment. To overcome these challenges on different levels, the field of industrial robotics is moving towards Human–Robot Collaboration (HRC) [1,2,3,4,5,6,7]. In the area of HRC, Robot Learning from Demonstration (LfD) [8] provides a natural and intuitive mechanism for humans to teach robots new skills without relying on professional knowledge. Robots first observe human demonstrations, extract task-relevant features, derive the optimal policy between the world states and actions, reproduce and generalize tasks in different situations, and refine policy during practice [9] (See Figure 1).
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