Learning Contact-Rich Manipulation Skills with Guided Policy Search (1501.05611v2)

Abstract: Autonomous learning of object manipulation skills can enable robots to acquire rich behavioral repertoires that scale to the variety of objects found in the real world. However, current motion skill learning methods typically restrict the behavior to a compact, low-dimensional representation, limiting its expressiveness and generality. In this paper, we extend a recently developed policy search method \cite{la-lnnpg-14} and use it to learn a range of dynamic manipulation behaviors with highly general policy representations, without using known models or example demonstrations. Our approach learns a set of trajectories for the desired motion skill by using iteratively refitted time-varying linear models, and then unifies these trajectories into a single control policy that can generalize to new situations. To enable this method to run on a real robot, we introduce several improvements that reduce the sample count and automate parameter selection. We show that our method can acquire fast, fluent behaviors after only minutes of interaction time, and can learn robust controllers for complex tasks, including putting together a toy airplane, stacking tight-fitting lego blocks, placing wooden rings onto tight-fitting pegs, inserting a shoe tree into a shoe, and screwing bottle caps onto bottles.

• The paper presents a guided policy search framework that combines linear-Gaussian controllers with neural network policies to learn contact-rich manipulation tasks.
• It employs adaptive learning schemes and trajectory optimization under unknown dynamics to enhance sample efficiency and robustness.
• Empirical results show effective generalization across intricate tasks, advancing autonomous robotics in both industrial and household settings.

An Examination of Learning Contact-Rich Manipulation Skills with Guided Policy Search

This paper by Levine, Wagener, and Abbeel explores a novel approach to robotic manipulation, addressing significant limitations in existing motion skill learning methods for autonomous robots. Specifically, it focuses on extending the capabilities of robotic systems to perform a range of complex and dynamic manipulation tasks using general policy representations. The proposed method deviates from conventional approaches by eschewing compact, low-dimensional policy representations, enabling robots to engage in contact-rich manipulation without preconceived models or demonstrations.

Methodology

The authors introduce a framework based on a guided policy search algorithm, which effectively combines sample-efficient techniques for learning linear-Gaussian controllers with more general-purpose policy representations, such as neural networks. The learning procedure iteratively refines a set of trajectory distributions using time-varying control policies that offer the flexibility to adapt to new contexts without explicit model reliance.

The paper details several methodological innovations to facilitate real-world robotic application:

1. Adaptive Learning Schemes: By employing an adaptive sample count and step size adjustment strategy, the method optimizes system interaction time. These mechanisms automatically calibrate the required sampling density based on the progress observed in the learning process, thus reducing unnecessary computational burdens and enhancing learning efficiency.
2. Trajectory Optimization under Unknown Dynamics: Leveraging locally linear models and an adjusted form of the standard LQG framework, the approach maintains constraint compliance while optimizing trajectory distributions. This hybrid model-based/model-free method gracefully navigates the trade-off between learning speed and robustness, particularly in dynamically complex environments.
3. Augmentation of Training Data: The research incorporates a synthetic sample generation technique to bolster neural network policy training with a limited real-world sample count. By deriving additional data from estimated state-action marginals, the neural network policies achieve greater robustness and generalization capabilities.

Results and Implications

The empirical evaluations demonstrate the effectiveness of the proposed method across several intricate manipulation tasks, such as assembling a toy airplane and inserting a shoe tree into a shoe. The introduction of neural network policies further facilitates the generalization of learned skills to a wider array of initial conditions, significantly forwarding the state of the art in robotic learning.

A notable aspect of the results is the confirmation of robust performance with limited interaction time. The system efficiently converges to adept handling of contact-rich tasks, indicative of significant improvements in sample efficiency compared to existing reinforcement learning methods.

Practical and Theoretical Implications

Practically, this approach holds substantial promise for diverse industrial applications, from manufacturing assembly tasks to personal household robotic aids. By enabling manipulation tasks devoid of detailed environmental models or pre-programmed instructions, it promises a step forward toward more autonomous, adaptable robotic systems.

On a theoretical level, the paper contributes to the understanding of high-dimensional policy learning in constrained environments, potentially paving the way for future research into dynamic task learning algorithms. It suggests new paradigms in reinforcement learning where sample efficiency is key, offering pertinent advancements in adaptive, model-free learning strategies.

Future Directions

The research suggests several avenues for future exploration, such as refining the integration of perception and control in policy learning. There is room to explore the joint optimization of sensing and actions to enhance policy robustness further. Additionally, the development of more sophisticated cost functions could refine task-specific learning objectives, nurturing broader adaptation in expansive task domains.

In conclusion, this paper represents a significant contribution to the field of autonomous robotic manipulation, presenting a coherent framework that addresses prevalent challenges and advances the operational capacity of robotic systems under dynamic, contact-rich conditions. The insights yielded herein lay a solid foundation for the continued advancement and refinement of autonomous learning paradigms in robotics.