Exploiting Symmetry and Heuristic Demonstrations in Off-policy Reinforcement Learning for Robotic Manipulation

Reinforcement learning demonstrates significant potential in automatically building control policies in numerous domains, but shows low efficiency when applied to robot manipulation tasks due to the curse of dimensionality. To facilitate the learning of such tasks, prior knowledge or heuristics that incorporate inherent simplification can effectively improve the learning performance. This paper aims to define and incorporate the natural symmetry present in physical robotic environments. Then, sample-efficient policies are trained by exploiting the expert demonstrations in symmetrical environments through an amalgamation of reinforcement and behavior cloning, which gives the off-policy learning process a diverse yet compact initiation. Furthermore, it presents a rigorous framework for a recent concept and explores its scope for robot manipulation tasks. The proposed method is validated via two point-to-point reaching tasks of an industrial arm, with and without an obstacle, in a simulation experiment study. A PID controller, which tracks the linear joint-space trajectories with hard-coded temporal logic to produce interim midpoints, is used to generate demonstrations in the study. The results of the study present the effect of the number of demonstrations and quantify the magnitude of behavior cloning to exemplify the possible improvement of model-free reinforcement learning in common manipulation tasks. A comparison study between the proposed method and a traditional off-policy reinforcement learning algorithm indicates its advantage in learning performance and potential value for applications

Exploiting Intrinsic Stochasticity of Real-Time Simulation to Facilitate Robust Reinforcement Learning for Robot Manipulation

Simulation is essential to reinforcement learning (RL) before implementation in the real world, especially for safety-critical applications like robot manipulation. Conventionally, RL agents are sensitive to the discrepancies between the simulation and the real world, known as the sim-to-real gap. The application of domain randomization, a technique used to fill this gap, is limited to the imposition of heuristic-randomized models. We investigate the properties of intrinsic stochasticity of real-time simulation (RT-IS) of off-the-shelf simulation software and its potential to improve the robustness of RL methods and the performance of domain randomization. Firstly, we conduct analytical studies to measure the correlation of RT-IS with the occupation of the computer hardware and validate its comparability with the natural stochasticity of a physical robot. Then, we apply the RT-IS feature in the training of an RL agent. The simulation and physical experiment results verify the feasibility and applicability of RT-IS to robust RL agent design for robot manipulation tasks. The RT-IS-powered robust RL agent outperforms conventional RL agents on robots with modeling uncertainties. It requires fewer heuristic randomization and achieves better generalizability than the conventional domain-randomization-powered agents. Our findings provide a new perspective on the sim-to-real problem in practical applications like robot manipulation tasks