Residual Reinforcement Learning for Robot Control (1812.03201v2)

Abstract: Conventional feedback control methods can solve various types of robot control problems very efficiently by capturing the structure with explicit models, such as rigid body equations of motion. However, many control problems in modern manufacturing deal with contacts and friction, which are difficult to capture with first-order physical modeling. Hence, applying control design methodologies to these kinds of problems often results in brittle and inaccurate controllers, which have to be manually tuned for deployment. Reinforcement learning (RL) methods have been demonstrated to be capable of learning continuous robot controllers from interactions with the environment, even for problems that include friction and contacts. In this paper, we study how we can solve difficult control problems in the real world by decomposing them into a part that is solved efficiently by conventional feedback control methods, and the residual which is solved with RL. The final control policy is a superposition of both control signals. We demonstrate our approach by training an agent to successfully perform a real-world block assembly task involving contacts and unstable objects.

Insights into Residual Reinforcement Learning for Robot Control

This paper presents a novel methodology for integrating conventional feedback control with reinforcement learning (RL) to address complex robot manipulation tasks, particularly those involving contact dynamics and external object interactions. The proposed approach, termed residual reinforcement learning, offers a solution that leverages the complementary strengths of classical feedback control and RL, aiming to improve adaptability and efficiency in real-world industrial applications.

Overview of the Methodology

The paper introduces a hybrid control architecture that decomposes the control task into two components: a classical feedback controller responsible for handling easily modeled rigid-body dynamics and an RL component tasked with managing the residuals, particularly those involving contact and variable dynamics that are challenging to model explicitly. The classical controller, leveraging predefined trajectories and path-following tasks, provides a structured base, while the RL component dynamically fine-tunes the control signals to adapt to environmental variations and unforeseen interactions.

The control strategy is formalized as a sum of two policy components: the traditional feedback controller $\pi_H(s_\text{m})$, and a learned residual policy $\pi_\theta(s_\text{m}, s_\text{o})$, designed to maximize a reward function $J = \E_{r_i, s_i \sim E, a_i \sim \pi}[R_0]$. This reward incorporates both positional objectives (path-following) and environmental interaction goals (maintaining stability and orientation of objects). This additive approach requires minimal modification to existing control protocols and integrates seamlessly with existing industrial automation systems.

Evaluation and Results

The effectiveness of the proposed methodology was rigorously tested through simulation and real-world experimentation using a complex block assembly task with a robot manipulator. Compared to traditional RL, residual RL demonstrated superior sample efficiency and stability, effectively learning to accommodate perturbations in block positioning and orientation without extensive manual tuning. The approach proved capable of rapidly adapting to both actuator noise and external variability, showcasing its potential for dynamic environments common in manufacturing.

In simulation, the method maintained performance under varying block orientations and control biases, significantly outperforming purely hand-engineered controllers and improving over RL algorithms without integrated priors. Similarly, real-world tests highlighted the rapid convergence of the method, achieving proficient task execution within three hours of training, a notable improvement over standard RL implementations.

Implications and Future Directions

The paper underscores the potential of residual reinforcement learning in mitigating some of the inherent challenges of deploying learning algorithms in real-world settings, such as safety concerns and data inefficiency. By incorporating predetermined control knowledge, this framework reduces the exploration burden on the RL system, leading to faster adaptation and increased robustness against unexpected variability.

The practical implications of this research are significant, particularly in enabling flexible and adaptive automation solutions. The integration of RL could allow robots to better handle complex assembly operations, enhancing the scope of applications in automated manufacturing.

Future research could explore incorporating more sophisticated perception models to further improve the robustness and general applicability of the system. As perceptual feedback is crucial for adaptive control, employing end-to-end learning frameworks that integrate visual inputs directly into the control policy could enhance the method's adaptability to new and more complex tasks. Additionally, exploring more diverse RL algorithms within this framework may yield further improvements in generalization and efficiency, expanding its usefulness across various industrial domains.