A probabilistic framework for tracking uncertainties in robotic manipulation
This work addresses the challenge of enabling robots to operate safely and successfully in unstructured and dynamic environments, but it appears incremental as it builds on common manipulation pipelines.
The authors tackled the problem of tracking uncertainties in robotic manipulation by proposing a probabilistic framework that decomposes the process into perception and physical interaction stages, demonstrating its benefit in an actual assembly task.
Precisely tracking uncertainties is crucial for robots to successfully and safely operate in unstructured and dynamic environments. We present a probabilistic framework to precisely keep track of uncertainties throughout the entire manipulation process. In agreement with common manipulation pipelines, we decompose the process into two subsequent stages, namely perception and physical interaction. Each stage is associated with different sources and types of uncertainties, requiring different techniques. We discuss which representation of uncertainties is the most appropriate for each stage (e.g. as probability distributions in SE(3) during perception, as weighted particles during physical interactions), how to convert from one representation to another, and how to initialize or update the uncertainties at each step of the process (camera calibration, image processing, pushing, grasping, etc.). Finally, we demonstrate the benefit of this fine-grained knowledge of uncertainties in an actual assembly task.