Introduction
In modern industrial automation, robots play a crucial role in performing complex assembly tasks. Among these tasks, pin-hole assembly represents a significant challenge due to its requirement for high precision and adaptability in dealing with complex contact dynamics. Traditional automation techniques often struggle with such tasks, as they demand extensive setup and reprogramming to accommodate new environments. Additionally, controlling contact forces is essential to prevent equipment damage, making the process even more complex.
Reinforcement learning (RL) has emerged as a promising approach for enabling robots to learn effective control strategies through trial and error, without requiring detailed knowledge of contact dynamics. However, RL-based methods face challenges during the initial stages of policy training, including low sampling efficiency and poor sample quality. These issues slow down algorithm convergence and increase the risk of the policy getting stuck in local optima. To address these limitations, this paper presents a novel robotic trajectory planning method that integrates prior knowledge with the Guided Policy Search (GPS) algorithm.
The proposed method leverages prior knowledge from human expert demonstrations and historical task data to initialize the policy, significantly improving learning efficiency. The GPS algorithm then optimizes this initial policy online, refining its precision and adaptability. Experimental validation on a robotic pin-hole assembly task demonstrates that this approach reduces training time and minimizes trial-and-error iterations, enabling robots to quickly acquire effective assembly strategies.
Problem Formulation
The pin-hole assembly task is fundamentally a specialized path-planning problem where the robot must navigate complex environmental and physical constraints while performing high-precision operations. Traditional trajectory planning methods often fall short in handling such tasks, whereas reinforcement learning offers an alternative solution for discovering optimal trajectories.
Markov Decision Process (MDP) Model
The pin-hole assembly task is modeled as a Markov Decision Process (MDP), where the robot’s motion trajectory is represented as a sequence of states and actions over time. Each time step consists of the robot’s state (joint angles, joint velocities, end-effector pose, and velocity) and the corresponding action (joint torques). The state transition model describes the probability distribution of the next state given the current state and action. The policy, which can be deterministic or stochastic, generates control actions based on the current state.
The goal of the MDP is to minimize the cumulative cost over the trajectory, where the cost function penalizes deviations from the target position and excessive control inputs. The cost function consists of two components:
- Position Deviation Cost: Encourages the end-effector to move toward the target position while ensuring precise placement.
- Control Input Cost: Minimizes joint torques to ensure smooth and stable motion.
By formulating the problem as an MDP, the robot can systematically learn and optimize its behavior, improving both success rate and efficiency in assembly tasks.
Methodology
The proposed method integrates prior knowledge with the GPS algorithm to enhance policy learning. The approach consists of three main components:
- Prior Knowledge Acquisition and Initialization
- Dynamic Model Fitting
- Policy Optimization via Guided Policy Search
Prior Knowledge Acquisition
Prior knowledge is obtained from two primary sources:
- Human Expert Demonstrations: High-quality trajectory data collected from expert demonstrations in free-space environments.
- Historical Task Data: Trajectories from previous assembly tasks, categorized based on their similarity to the current task.
These trajectories are stored in an experience replay buffer, which is dynamically updated during training to balance prior knowledge with newly collected data. This ensures that the robot benefits from expert guidance while gradually adapting to new environments.
Dynamic Model Fitting
The dynamic model, which predicts the next state given the current state and action, is initialized using prior knowledge. Two modeling approaches are employed:
- Time-Varying Linear Model (TVLM): A probabilistic model that captures environmental dynamics with Gaussian uncertainty.
- Gaussian Mixture Model (GMM): Used to approximate nonlinear dynamics by clustering similar states and actions.
These models enable efficient policy learning by reducing the need for extensive random exploration.
Policy Optimization via Guided Policy Search
The GPS algorithm optimizes the initial policy through iterative refinement. Key steps include:
- Trajectory Sampling: The current policy is used to collect new trajectory samples.
- Dynamic Model Update: The experience buffer is updated with new data, and the dynamic model is refined.
- Policy Improvement: The Linear Quadratic Gaussian (LQG) method optimizes the policy while ensuring it does not deviate too far from the previous version (using KL-divergence constraints).
This iterative process enhances the policy’s precision and adaptability, enabling the robot to handle complex assembly tasks effectively.
Experimental Results
The proposed method was evaluated on the Agile Robotics for Industrial Automation Competition (ARIAC) benchmark, using a Kinova Gen3 6-DOF robotic arm. The task involved assembling a regulator workpiece into a corresponding hole with tight tolerances.
Performance Comparison
Three methods were compared:
- Proposed Method (Prior Knowledge + GPS): Achieved convergence in 7-8 iterations, significantly faster than baseline methods.
- Standard GPS: Required 12-14 iterations to converge.
- Proximal Policy Optimization (PPO): Took over 50 iterations to achieve comparable performance.
The results demonstrated that integrating prior knowledge with GPS accelerates policy learning while maintaining high precision.
Policy Effectiveness
- Initial Policy from Prior Knowledge: Provided a reasonable starting point but lacked the precision needed for successful assembly.
- Optimized Policy via GPS: Achieved near-perfect alignment, enabling successful insertion with minimal deviation.
These findings highlight the importance of combining prior knowledge with iterative policy refinement for complex robotic tasks.
Conclusion
This paper presented a novel approach for robotic pin-hole assembly that integrates prior knowledge with guided policy search. By leveraging expert demonstrations and historical data, the method significantly reduces the training time and improves policy performance. The GPS algorithm further refines the policy, ensuring adaptability to dynamic environments.
Future work will extend this method to irregularly shaped or deformable parts and incorporate vision-based perception for richer data acquisition. The integration of prior knowledge with reinforcement learning represents a promising direction for advancing robotic automation in industrial applications.
DOI: 10.19734/j.issn.1001-3695.2024.08.0324
Was this helpful?
0 / 0