In this work we solve for partially observable reinforcement learning (RL) environments by adding recurrency. We focus on partially observable robotic assembly tasks in the continuous action domain , with force/torque sensing being the only observation. We have developed a new distributed RL agent, named Recurrent Distributed DDPG (RD2), which adds a recurrent neural network layer to Ape-X DDPG  and makes two important improvements on prioritized experience replay to stabilize training. We demonstrate the effectiveness of RD2 on a variety of joint assembly tasks and a partially observable version of the pendulum task from OpenAI Gym. Our results show that RD2 is able to achieve better performance than Ape-X DDPG and PPO with LSTM on partially observable tasks with varying complexity. We also show that the trained models adapt well to different initial states and different types of noise injected in the simulated environment . The video presenting our experiments is available at URL
In this work we propose a learning approach to high-precision robotic assembly problems in the continuous action domain . Unlike many learning-based approaches that heavily rely on vision or spatial tracking, our approach takes force/torque as the only observation. Each learned policy from our approach is robot-agnostic, which can be applied to different robotic arms. These two features can greatly reduce complexity and cost to perform robotic assembly in the real world, especially in unstructured settings such as in architectural construction. To achieve it, we have developed a new distributed RL agent, named Recurrent Distributed DDPG (RD2), which extends Ape-X DDPG with recurrency and makes two structural improvements on prioritized experience replay . Our results show that RD2 is able to solve two fundamental high-precision assembly tasks, lap-joint and peg-in-hole, and outperforms two state-of-the-art algorithms, Ape-X DDPG and PPO with LSTM . We have successfully evaluated our robot-agnostic policies on three robotic arms, Kuka KR60, Franka Panda, and UR10, in simulation . The video presenting our experiments is available at URL
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Each learned policy from our approach is robot-agnostic, which can be applied to different robotic arms. These two features can greatly reduce complexity and cost to perform robotic assembly in the real world, especially in unstructured settings such as in architectural construction. To achieve it, we
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