Twisting Lids Off with Two Hands
Manipulating objects with two multi-fingered hands has been a long-standing
challenge in robotics, attributed to the contact-rich nature of many
manipulation tasks and the complexity inherent in coordinating a
high-dimensional bimanual system. In this work, we consider the problem of
twisting lids of various bottle-like objects with two hands, and demonstrate
that policies trained in simulation using deep reinforcement learning can be
effectively transferred to the real world. With novel engineering insights into
physical modeling, real-time perception, and reward design, the policy
demonstrates generalization capabilities across a diverse set of unseen
objects, showcasing dynamic and dexterous behaviors. Our findings serve as
compelling evidence that deep reinforcement learning combined with sim-to-real
transfer remains a promising approach for addressing manipulation problems of
unprecedented complexity.
65e75f39cd00e92129df0b5f
Toru Lin
65e75f39cd00e92129df0b60
Zhao-Heng Yin
65e75f39cd00e92129df0b61
Haozhi Qi
admin_assigned
2024-03-05T21:01:48.975Z
658871e7d1331d552be8b457
/avatars/c323178ad1e0fbdfa5e66dcd4ba357e6.svg
Haozhi Qi
user
HaozhiQi
65e75f39cd00e92129df0b62
Pieter Abbeel
65e75f39cd00e92129df0b63
Jitendra Malik
admin_assigned
2024-03-05T21:02:02.344Z
65369a95605a07338de78ab0
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/sGFjOjLT2akN-sn5beVWL.jpeg
Jitendra Malik 
user
jitendra1995
Twisting Lids Off with Two Hands
Manipulating objects with two multi-fingered hands has been a long-standing
challenge in robotics, attributed to the contact-rich nature of many
manipulation tasks and the complexity inherent in coordinating a
high-dimensional bimanual system. In this work, we consider the problem of
twisting lids of various bottle-like objects with two hands, and demonstrate
that policies trained in simulation using deep reinforcement learning can be
effectively transferred to the real world. With novel engineering insights into
physical modeling, real-time perception, and reward design, the policy
demonstrates generalization capabilities across a diverse set of unseen
objects, showcasing dynamic and dexterous behaviors. Our findings serve as
compelling evidence that deep reinforcement learning combined with sim-to-real
transfer remains a promising approach for addressing manipulation problems of
unprecedented complexity.
Deep reinforcement learning policies trained in simulation can effectively transfer to real-world bimanual object manipulation tasks, such as twisting bottle lids, with robust generalization to unseen objects.
deep reinforcement learning sim-to-real transfer bimanual manipulation dynamic behaviors dexterous behaviors