Introduction
Robotic manipulators have become increasingly important in modern manufacturing, playing critical roles in industries such as automotive, electronics, and logistics. One of the key challenges in robotic control is achieving precise trajectory tracking, especially in environments with nonlinear disturbances and varying conditions. Traditional control methods, such as PID controllers and adaptive sliding mode control, often rely on accurate system models, which can be difficult to obtain in real-world applications.
Recent advances in deep reinforcement learning (DRL) have shown promise in addressing these challenges by enabling robots to learn control policies through interaction with their environment. However, single-agent DRL approaches often struggle with generalization and robustness when faced with unseen trajectories or strong disturbances. To overcome these limitations, this paper introduces a novel multi-agent deep reinforcement learning (MDRL) framework combined with behavior cloning (BC) to enhance both tracking performance and adaptability in uncertain environments.
Background and Related Work
Challenges in Robotic Manipulator Control
Robotic manipulators operate in dynamic environments where uncertainties, such as external disturbances and modeling inaccuracies, can significantly degrade performance. Traditional control strategies, including model-based adaptive control and neural network-based approaches, have been widely studied. However, these methods often require extensive system identification or suffer from training inefficiencies.
Deep Reinforcement Learning in Robotics
DRL has emerged as a powerful tool for robotic control, enabling agents to learn optimal policies through trial and error. Algorithms like Deep Deterministic Policy Gradient (DDPG) and Twin Delayed Deep Deterministic Policy Gradient (TD3) have been successfully applied to continuous control tasks, including robotic arm trajectory tracking. However, single-agent DRL methods tend to specialize in specific trajectories seen during training, limiting their generalization capabilities.
Multi-Agent Reinforcement Learning
MDRL extends single-agent reinforcement learning by decomposing complex tasks into subtasks handled by multiple agents. This approach has been used in robotic applications such as multi-arm coordination and assembly tasks. However, training multiple agents simultaneously introduces challenges, including non-stationarity and coordination difficulties.
Proposed Methodology
System Overview
The proposed MDRL framework consists of two specialized agents:
- PID Agent: This agent adjusts the parameters of a PID controller, which then generates torque commands for the manipulator. The PID controller enhances the system’s generalization ability by providing stable feedback control.
- DDR Agent (Direct Deep Reinforcement Learning Agent): This agent directly outputs torque commands to compensate for disturbances and improve tracking stability.
By combining these two agents, the system leverages the strengths of both model-based and model-free control strategies.
Behavior Cloning for Training Acceleration
Training multiple agents simultaneously is challenging due to the increased complexity and exploration requirements. To address this, behavior cloning is used to pre-train the PID agent using expert demonstrations from a conventional PID controller. This pre-training phase ensures that the PID agent starts with a reasonable policy, reducing the exploration burden during reinforcement learning.
Reward Function Design
Each agent has a distinct reward function tailored to its role:
- PID Agent Reward: Focuses on minimizing tracking error to ensure overall trajectory accuracy.
- DDR Agent Reward: Incorporates both tracking error and error rate to prioritize disturbance rejection and smooth control.
This dual-reward structure ensures that both agents work synergistically toward the common goal of accurate trajectory tracking.
Experimental Setup
Simulation Environment
A two-degree-of-freedom robotic arm was modeled using Euler-Lagrange dynamics. The system was subjected to random nonlinear disturbances to evaluate robustness. The disturbances were designed to simulate real-world uncertainties, such as sudden force variations.
Training and Evaluation
The proposed method was compared against baseline approaches, including standalone TD3 and TD3 with PID control. Training was conducted over multiple episodes, with performance evaluated based on tracking accuracy and disturbance rejection.
Results and Discussion
Tracking Performance
The MDRL framework demonstrated superior tracking accuracy compared to single-agent methods. The PID agent ensured stable trajectory following, while the DDR agent effectively compensated for disturbances.
Robustness to Disturbances
In environments with random disturbances, the proposed method maintained consistent performance, whereas standalone TD3 exhibited significant tracking errors. The combination of PID feedback and direct torque compensation proved highly effective in rejecting disturbances.
Generalization to Unseen Trajectories
A key advantage of the MDRL approach was its ability to generalize to trajectories not encountered during training. The PID agent’s adaptability allowed the system to handle diverse motion patterns, while the DDR agent ensured robustness against variations.
Conclusion
This paper presented a novel multi-agent deep reinforcement learning framework for robotic manipulator trajectory tracking. By integrating a PID agent and a DDR agent, the system achieved both high tracking accuracy and robustness to disturbances. Behavior cloning was employed to accelerate training, addressing the challenges of multi-agent coordination.
Experimental results demonstrated the effectiveness of the proposed method in various scenarios, including environments with strong nonlinear disturbances and previously unseen trajectories. The framework’s ability to generalize and adapt makes it a promising solution for real-world robotic applications.
Future work will focus on extending the approach to higher-dimensional manipulators and real-world hardware implementations.
DOI: 10.19734/j.issn.1001-3695.2024.09.0340
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