Learning and Inferring Movement with Deep Generative Model
This work addresses movement planning for robotics, but it appears incremental as it builds on existing base methods with specific improvements.
The paper tackles the challenge of learning and inferring movement in high-dimensional, environment-dependent scenarios by proposing a probabilistic method using a directed graphic model and deep generative model, which shows advantages in robotic approaching path planning tasks with limited training data.
Learning and inference movement is a very challenging problem due to its high dimensionality and dependency to varied environments or tasks. In this paper, we propose an effective probabilistic method for learning and inference of basic movements. The motion planning problem is formulated as learning on a directed graphic model and deep generative model is used to perform learning and inference from demonstrations. An important characteristic of this method is that it flexibly incorporates the task descriptors and context information for long-term planning and it can be combined with dynamic systems for robot control. The experimental validations on robotic approaching path planning tasks show the advantages over the base methods with limited training data.