Robust Contact-rich Manipulation through Implicit Motor Adaptation

2024-12-16

arXiv.org

AI Summary

To address the challenges of policy generalization and robustness arising from physical parameter uncertainty in contact-intensive manipulation tasks, this paper proposes an implicit motion adaptation framework. Unlike existing approaches relying on explicit system identification or online policy retraining, our method implicitly encodes parameter-conditioned base policies using tensor train (TT) decomposition, enabling efficient policy retrieval under coarse prior distributions. We introduce a decoupled tensor kernel optimization scheme and provide theoretically grounded robustness guarantees. Extensive evaluations—spanning simulation and real-robot experiments across three contact manipulation primitives—demonstrate strong generalization and robustness across diverse physical instances, without requiring precise modeling or additional training. The framework simultaneously achieves near-optimal performance, broad generalization capability, and high deployment efficiency.

Technology Category

• Intelligent Robots: Manipulation
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Adversarial Learning & Robustness

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Contact-rich manipulation plays an important role in daily human activities. However, uncertain physical parameters often pose significant challenges for both planning and control. A promising strategy is to develop policies that are robust across a wide range of parameters. Domain adaptation and domain randomization are widely used, but they tend to either limit generalization to new instances or perform conservatively due to neglecting instance-specific information. extit{Explicit motor adaptation} addresses these issues by estimating system parameters online and then retrieving the parameter-conditioned policy from a parameter-augmented base policy. However, it typically requires precise system identification or additional training of a student policy, both of which are challenging in contact-rich manipulation tasks with diverse physical parameters. In this work, we propose extit{implicit motor adaptation}, which enables parameter-conditioned policy retrieval given a roughly estimated parameter distribution instead of a single estimate. We leverage tensor train as an implicit representation of the base policy, facilitating efficient retrieval of the parameter-conditioned policy by exploiting the separable structure of tensor cores. This framework eliminates the need for precise system estimation and policy retraining while preserving optimal behavior and strong generalization. We provide a theoretical analysis to validate the approach, supported by numerical evaluations on three contact-rich manipulation primitives. Both simulation and real-world experiments demonstrate its ability to generate robust policies across diverse instances. Project website: href{https://sites.google.com/view/implicit-ma}{https://sites.google.com/view/implicit-ma}.

Problem

• Addresses uncertain physical parameters in contact-rich manipulation
• Eliminates need for precise system estimation and policy retraining
• Enables robust policy generation across diverse physical instances

Innovation

• Implicit motor adaptation for robust manipulation
• Tensor train for efficient policy retrieval
• No precise system estimation or retraining needed