Stanford Vision and Learning Lab
@StanfordSVL
A thorough evaluation of possible action spaces and their efficiency with RL for manipulation tasks. The answer is best of both worlds - Op. Space control in end effector space with learnable gains fares better than end to end image-to-torque
Animesh Garg@animesh_garg · Aug 7, 2019Which control spaces work well for RL in contact-rich robot manipulation -- 'Variable Impedance Control in End-Effector Space' #IROS2019
Paper: arxiv.org/abs/1906.08880
Video: youtu.be/AozIUIW3Ghs
w\ Roberto M-M., @michellearning, R. Gardener, @silviocinguetta, @leto__jean
Paper: arxiv.org/abs/1906.08880
Video: youtu.be/AozIUIW3Ghs
w\ Roberto M-M., @michellearning, R. Gardener, @silviocinguetta, @leto__jean
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