Learning from Symmetry: Meta-Reinforcement Learning with
Symmetrical Behaviors and Language Instructions
Xiangtong Yao, Zhenshan Bing, Genghang Zhuang, Kejia Chen, Hongkuan Zhou, Kai Huang, and Alois Knoll
Video of simulation and real-world experiments
Real-world experiments
Real-world experiment scenario. We use Intel RealSense D435i cameras to locate the red object.
Push-right and Push-left
Push-right tasks
Task goal position: [0.1, 0.7, 0]
Task goal position: [0.05, 0.65, 0]
Push-left tasks
Task goal position: [-0.1, 0.7, 0]
Task goal position: [-0.05, 0.65, 0]
We adapt Panda robot to Meta-world environment. For more information, please visit
Supplementary materials
The recovered reward function of other task families
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The visualisation of trained AIRL policies, each of which is trained by symmetrical trajectories generated from the Symmetric Data Generator.
Language instructions of meta-training tasks and symmetry tasks
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The visualisation of meta-training tasks and meta-test tasks. The setting of the above tasks are the same as those of the Meta-world benchmark.