English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Simulation has recently become key for deep reinforcement learning to safelyand efficiently acquire general and complex control policies from visual andproprioceptive inputs. Tactile information is not usually considered despiteits direct relation to environment interaction. In this work, we present asuite of simulated environments tailored towards tactile robotics andreinforcement learning. A simple and fast method of simulating optical tactilesensors is provided, where high-resolution contact geometry is represented asdepth images. Proximal Policy Optimisation (PPO) is used to learn successfulpolicies across all considered tasks. A data-driven approach enablestranslation of the current state of a real tactile sensor to correspondingsimulated depth images. This policy is implemented within a real-time controlloop on a physical robot to demonstrate zero-shot sim-to-real policy transferon several physically-interactive tasks requiring a sense of touch.

Tactile Sim-to-Real Policy Transfer via Real-to-Sim Image Translation