Conditional Neural Expert Processes for Learning from Demonstration
Yigit Yildirim Emre Ugur
Learning from Demonstration (LfD) is a widely used technique for skill
acquisition in robotics. However, demonstrations of the same skill may exhibit
significant variances, or learning systems may attempt to acquire different
means of the same skill simultaneously, making it challenging to encode these
motions into movement primitives. To address these challenges, we propose an
LfD framework, namely the Conditional Neural Expert Processes (CNEP), that
learns to assign demonstrations from different modes to distinct expert
networks utilizing the inherent information within the latent space to match
experts with the encoded representations. CNEP does not require supervision on
which mode the trajectories belong to. Provided experiments on artificially
generated datasets demonstrate the efficacy of CNEP. Furthermore, we compare
the performance of CNEP with another LfD framework, namely Conditional Neural
Movement Primitives (CNMP), on a range of tasks, including experiments on a
real robot. The results reveal enhanced modeling performance for movement
primitives, leading to the synthesis of trajectories that more accurately
reflect those demonstrated by experts, particularly when the model inputs
include intersection points from various trajectories. Additionally, CNEP
offers improved interpretability and faster convergence by promoting expert
specialization. Furthermore, we show that the CNEP model accomplishes obstacle
avoidance tasks with a real manipulator when provided with novel start and
destination points, in contrast to the CNMP model, which leads to collisions
with the obstacle.