Manipulation by Feel: Touch-Based Control with Deep Predictive Models
This addresses the problem of enabling dexterous manipulation for robots in unstructured environments, though it is incremental as it builds on existing tactile sensing and MPC methods.
The paper tackles the challenge of using tactile sensing for continuous, non-prehensile robotic manipulation by proposing deep tactile MPC, a framework that learns tactile servoing from raw sensor inputs without manual supervision. The result is a robot successfully manipulating objects like a ball, analog stick, and 20-sided die to user-specified configurations based on goal tactile readings.
Touch sensing is widely acknowledged to be important for dexterous robotic manipulation, but exploiting tactile sensing for continuous, non-prehensile manipulation is challenging. General purpose control techniques that are able to effectively leverage tactile sensing as well as accurate physics models of contacts and forces remain largely elusive, and it is unclear how to even specify a desired behavior in terms of tactile percepts. In this paper, we take a step towards addressing these issues by combining high-resolution tactile sensing with data-driven modeling using deep neural network dynamics models. We propose deep tactile MPC, a framework for learning to perform tactile servoing from raw tactile sensor inputs, without manual supervision. We show that this method enables a robot equipped with a GelSight-style tactile sensor to manipulate a ball, analog stick, and 20-sided die, learning from unsupervised autonomous interaction and then using the learned tactile predictive model to reposition each object to user-specified configurations, indicated by a goal tactile reading. Videos, visualizations and the code are available here: https://sites.google.com/view/deeptactilempc