Reinforcement Learning on Variable Impedance Controller for High-Precision Robotic Assembly (1903.01066v2)

Abstract: Precise robotic manipulation skills are desirable in many industrial settings, reinforcement learning (RL) methods hold the promise of acquiring these skills autonomously. In this paper, we explicitly consider incorporating operational space force/torque information into reinforcement learning; this is motivated by humans heuristically mapping perceived forces to control actions, which results in completing high-precision tasks in a fairly easy manner. Our approach combines RL with force/torque information by incorporating a proper operational space force controller; where we also exploit different ablations on processing this information. Moreover, we propose a neural network architecture that generalizes to reasonable variations of the environment. We evaluate our method on the open-source Siemens Robot Learning Challenge, which requires precise and delicate force-controlled behavior to assemble a tight-fit gear wheel set.

Reinforcement Learning on Variable Impedance Controller for High-Precision Robotic Assembly

The paper "Reinforcement Learning on Variable Impedance Controller for High-Precision Robotic Assembly" by Jianlan Luo et al. focuses on leveraging reinforcement learning (RL) to enhance the control strategies of robots engaged in high-precision assembly tasks. Specifically, the work investigates how RL can be utilized to automate the skill acquisition of robots and improve their ability to interact precisely with objects, mimicking complex human-like manipulation strategies.

Technical Overview

The authors introduce a methodology that combines RL with operational space force/torque information to tackle the challenges of precise robotic assembly. The paper centers around a variable impedance controller, whereby the robot adjusts its forces dynamically across different phases of the task. This approach is rooted in the hypothesis that operational space force controllers, akin to how humans use tactile feedback to perform tasks, can facilitate autonomous and adaptable robot behavior.

An iterative Linear-Quadratic-Gaussian (iLQG) control algorithm is employed to generate control actions based on state observations. The controller's adaptability to different assembly situations is tested using the Siemens Robot Learning Challenge, requiring delicate force-controlled interactions.

Numerical Results

The paper presents robust results of the applied methods across diverse assembly tasks, highlighting the significant improvement over traditional kinematic controllers and purely torque-based RL approaches. For instance, the success rates in assembling gear sets with tight tolerance achieved by the proposed method were substantially higher: 100% success in tasks 1 and 2, and notable improvements in tasks 3 and 4 compared to other methods.

Bold Claims

One of the bold claims is the ability of the RL-based controller to automate the discovery of Pfaffian constraints—a formalism representing task-specific restrictions—through continuous interactions with the environment. This capability effectively guides the robot in navigating through varied and complex assembly scenarios autonomously. Additionally, a noteworthy assertion is that the newly introduced neural network architecture can leverage force/torque data for better adaptability to environmental variations.

Implications and Future Directions

The implications of this research are substantial for industrial robotics, where the necessity for precision and adaptability is paramount. The proposed methods contribute towards minimizing manual intervention in programming robots for each specific task, ultimately enhancing productivity and performance in manufacturing processes. Moreover, this research opens a pathway for more complex integration of sensory inputs, such as vision and tactile sensing, in end-to-end neural network architectures for comprehensive environment interaction.

In future developments, the integration of raw sensory data could further refine the decision-making process, allowing robots to initiate operations from diverse starting conditions with increased efficacy. Another prospective direction is the explicit modeling of environmental contact information, which could lead to reduced sample complexity and facilitate efficient policy transfer across different robotic platforms.

Overall, the paper presents a significant advancement in the application of RL for complex and precision-demanding robotic assembly jobs, setting a strong foundation for continued exploration and development in adaptive robotic behaviors.