Goal-Conditioned Terminal Value Estimation for Real-time Multi-task Model Predictive Control Using a Surrogate Robot Model

핵심 개념

This paper introduces a novel hierarchical Model Predictive Control (MPC) framework that leverages goal-conditioned terminal value learning and a surrogate robot model to achieve real-time, multi-task control of complex robotic systems.

초록

• Bibliographic Information: Morita, M., Yamamori, S., Yagi, S., Sugimoto, N., & Morimoto, J. (2024). Goal-Conditioned Terminal Value Estimation for Real-time and Multi-task Model Predictive Control. arXiv preprint arXiv:2410.04929.
• Research Objective: This research aims to address the limitations of traditional MPC in handling high-dimensional systems and dynamically changing tasks by proposing a novel hierarchical control framework with goal-conditioned terminal value learning.
• Methodology: The researchers developed a two-stage learning framework. The lower layer learns goal-conditioned terminal values using a surrogate robot model for faster computation. The upper layer uses a trajectory generator (Kanayama Control in this case) to provide goal trajectories to the lower layer. Domain randomization is employed during training to enhance robustness. The proposed method is evaluated on a simulated bipedal inverted pendulum robot (Diablo) in path tracking tasks on flat and sloped terrains.
• Key Findings: The proposed method successfully achieved real-time control of the robot, enabling it to follow a lemniscate trajectory on both flat and sloped surfaces. The use of goal-conditioned terminal values allowed for dynamic adaptation to changing objectives, while the surrogate robot model significantly reduced computation time. Domain randomization proved crucial in enhancing the robustness of the learned terminal values, particularly during challenging terrain transitions.
• Main Conclusions: The hierarchical MPC framework with goal-conditioned terminal value learning and a surrogate robot model offers a promising solution for real-time, multi-task control of complex robotic systems. The approach effectively balances computational efficiency with control performance and adaptability to varying environments and objectives.
• Significance: This research contributes to the advancement of MPC techniques for real-world robotic applications by enabling real-time control of high-dimensional systems in complex, dynamic environments. The proposed framework has the potential to enhance the capabilities of robots in various domains, including locomotion, manipulation, and autonomous navigation.
• Limitations and Future Research: The current study focuses on simulations. Future research should validate the proposed method on real-world robotic platforms. Further exploration of different trajectory generators and domain randomization techniques could further improve the framework's performance and generalizability.

통계

The robot model used has a state dimension of 33 and 6 control variables.
The control cycle was set at 10 ms.
The maximum computation time for the control framework was 7.7 ms on flat terrain and 8.7 ms on sloped terrain.
In the robustness evaluation, forces of 20 N and 40 N were applied to the base link for 2 seconds.

인용구

"This study proposes a flexible control method that can change the control target without greatly simplifying the robot model while keeping the computational cost of MPC low."
"By adopting the idea of goal-conditioned reinforcement learning, our proposed method learns the terminal value with goal-related variables as inputs."
"The proposed hierarchical approach allows for dynamic switching of objectives in response to the surrounding context, thus generating flexible and diverse robot behaviors."

핵심 통찰 요약

Goal-Conditioned Terminal Value Estimation for Real-time and Multi-task Model Predictive Control

by Mitsuki Mori... 게시일 arxiv.org 10-08-2024

https://arxiv.org/pdf/2410.04929.pdf

더 깊은 질문

How can this hierarchical MPC framework be adapted for more complex tasks, such as manipulation or collaboration with other robots?

This hierarchical MPC framework, with its goal-conditioned terminal value learning and surrogate model approach, presents a solid foundation for extension to more complex robotic tasks like manipulation or multi-robot collaboration. Here's a breakdown of potential adaptations:
1. Manipulation Tasks:

Goal Variable Redefinition: For manipulation, the goal variables would need to encompass the object's state (position, orientation) and potentially contact forces. This could involve using visual servoing techniques to represent the desired object pose as the goal.
Action Space Augmentation: The action space would need to include not just joint velocities but also end-effector forces or impedances to enable dexterous manipulation.
Cost Function Modification: The cost function should incorporate aspects like grasping stability, object trajectory tracking, and collision avoidance with the environment and potentially other agents.
Surrogate Model Complexity: The surrogate model might need to represent the robot's arm dynamics more accurately, potentially incorporating simplified hand models for grasping interactions.
2. Multi-Robot Collaboration:

Decentralized Control: Each robot could have its own hierarchical MPC controller, with the upper level coordinating goals and exchanging information about planned trajectories to avoid collisions and achieve shared objectives.
Shared Goal Variables: Goal variables could represent the joint state of multiple robots or the state of a common object they are manipulating.
Communication Constraints: The framework should account for potential limitations in communication bandwidth between robots, perhaps by optimizing communication frequency or using predictive models of other agents' behavior.
Additional Considerations:

Learning from Demonstrations: For complex tasks, it might be beneficial to initialize the terminal value function using data from human demonstrations or pre-trained policies.
Scalability: As the number of robots or the complexity of the task increases, efficient methods for solving the MPC optimization problem and coordinating actions become crucial.
By carefully adapting the goal variables, action space, cost function, and potentially incorporating decentralized control strategies, this hierarchical MPC framework can be extended to tackle the challenges of manipulation and multi-robot collaboration.

While domain randomization improves robustness, could it potentially limit the accuracy of the learned terminal values in specific, well-defined environments?

You are right to point out the potential trade-off between robustness and accuracy when using domain randomization (DR) for terminal value learning in MPC.
Here's why DR might limit accuracy in specific environments:

Over-generalization: By training on a wide range of randomized environments, the learned terminal value function might become overly general. It might prioritize handling unlikely scenarios at the expense of optimal performance in the specific, well-defined target environment.
Bias towards common features: If the randomization process doesn't adequately cover the full spectrum of potential variations, the learned terminal value might be biased towards the more frequently encountered randomized features, potentially leading to suboptimal actions in less common but critical situations within the target environment.
Mitigation Strategies:

Targeted Domain Randomization: Instead of completely randomizing the environment, focus on variations that are relevant to the target environment. For example, if the target environment is a sloped surface, randomize the slope angle within a realistic range rather than introducing irrelevant variations like object shapes.
Curriculum Learning: Start with a narrow range of environmental variations during training and gradually increase the randomization range. This allows the terminal value function to first learn accurate values for the most critical scenarios before generalizing to a wider range of situations.
Hybrid Approach: Combine DR with data from the specific target environment. This can be achieved by fine-tuning the terminal value function using data collected from the target environment after initial training with DR.
In essence, while DR is a powerful technique for improving robustness, it's crucial to carefully consider the trade-off with accuracy in specific environments. By employing targeted randomization, curriculum learning, or hybrid approaches, we can mitigate the potential limitations and achieve a balance between robustness and accuracy in terminal value learning for MPC.

If we view the robot's control system as a metaphor for human decision-making, how might the concepts of "goal-conditioned learning" and "surrogate models" translate to our understanding of human behavior and cognition?

The concepts of "goal-conditioned learning" and "surrogate models" from this hierarchical MPC framework offer intriguing parallels to human decision-making and cognition:
1. Goal-Conditioned Learning:

Goal-Directed Behavior: Humans are inherently goal-oriented. Our actions are often driven by a desire to achieve specific objectives, whether it's grabbing a cup of coffee or finishing a work project. This aligns with the idea of goal-conditioned learning, where our past experiences and the value we associate with different outcomes shape our choices in pursuit of desired goals.
Adaptability and Flexibility: Just like the robot adjusting its behavior based on changing goal variables, humans demonstrate remarkable adaptability in their decision-making. We can switch between different goals, modify our strategies based on feedback, and learn new skills to achieve desired outcomes in diverse situations.
2. Surrogate Models:

Mental Models: Humans constantly build and refine internal models of the world around us. These mental models, while simplified representations of reality, allow us to predict the consequences of our actions, plan for the future, and make sense of complex situations. This mirrors the use of surrogate models in the MPC framework, where a computationally lighter model enables faster decision-making.
Cognitive Biases: However, our reliance on simplified mental models can also lead to cognitive biases and errors in judgment. Just as the surrogate model might not perfectly capture all the complexities of the real robot, our mental models can be influenced by our limited experiences, assumptions, and subjective interpretations, potentially leading to suboptimal decisions.
Connecting the Concepts:
The interplay of goal-conditioned learning and surrogate models suggests that human decision-making is a dynamic process, constantly balancing the pursuit of goals with our internal representations of the world. We learn from our experiences, update our beliefs, and adjust our actions to navigate complex environments and achieve desired outcomes.
Further Implications:

Understanding Human Error: This framework can provide insights into why humans make mistakes. Just as the robot might fall with an inaccurate surrogate model, our cognitive biases stemming from flawed mental models can lead to errors in judgment and decision-making.
Improving Decision-Making: By recognizing the limitations of our internal models and actively seeking out diverse experiences and perspectives, we can potentially improve the accuracy of our surrogate models and make more informed decisions.
In conclusion, while not a perfect analogy, viewing human decision-making through the lens of goal-conditioned learning and surrogate models offers a valuable framework for understanding our behavior, our capacity for adaptation, and the potential pitfalls of our cognitive processes.