  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.
