Abstract
Policy learning and compliant control offer a promising route to reliable autonomous assembly under pose errors and contact uncertainty. However, combining them does not ensure coordination: the policy may continue pushing against contact while the controller yields, producing sustained loading with limited progress. To address this problem, we propose LeCo (Leverage Compliance), a policy-admittance learning framework that guides a visual policy through execution-time interaction under fixed admittance. A multirate feedback mechanism aggregates high-rate contact-interaction records into policy-transition rewards. An integrated conflict cost then characterizes sustained policy-loading/controller-unloading opposition, while a directional high-force tail cost captures continued-loading events within a transition. Together with task completion, these costs encourage the policy to leverage compliance with less unproductive loading. We evaluate LeCo on four real connector-assembly tasks, obtaining an aggregate success rate of 94%. Across tasks, mean successful-trial resultant-force and torque peaks decrease by approximately 30% and 64% relative to the comparison baseline. Reward ablation further shows that adding conflict shaping reduces median successful-trial contact-conditioned conflict density by approximately 53%. These results support learning to leverage fixed compliance by turning multirate policy-admittance interaction into complementary reward signals for effective, lower-load insertion.
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Publication details
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- Open access
- Green open access
Cite this article
APA 7
Wang, C., Li, M., Dai, H., Lin, Z., Wei, S., & Yue, X. (2026). Learning to Leverage Compliance: A Policy-Admittance Learning Framework for Robotic Insertion. https://omanscience.com/en/articles/learning-to-leverage-compliance-a-policy-admittance-learning-framework-for-robotic-insertion
MLA 9
Wang, Chongren, et al. "Learning to Leverage Compliance: A Policy-Admittance Learning Framework for Robotic Insertion." https://omanscience.com/en/articles/learning-to-leverage-compliance-a-policy-admittance-learning-framework-for-robotic-insertion.
Chicago (author–date)
Wang, Chongren, Minghe Li, Honghua Dai, Zhicheng Lin, Shiyang Wei, and Xiaokui Yue. 2026. "Learning to Leverage Compliance: A Policy-Admittance Learning Framework for Robotic Insertion." https://omanscience.com/en/articles/learning-to-leverage-compliance-a-policy-admittance-learning-framework-for-robotic-insertion.
Harvard
Wang, C., Li, M., Dai, H., Lin, Z., Wei, S. and Yue, X. (2026) 'Learning to Leverage Compliance: A Policy-Admittance Learning Framework for Robotic Insertion', Available at: https://omanscience.com/en/articles/learning-to-leverage-compliance-a-policy-admittance-learning-framework-for-robotic-insertion.
Vancouver
Wang C, Li M, Dai H, Lin Z, Wei S, Yue X. Learning to Leverage Compliance: A Policy-Admittance Learning Framework for Robotic Insertion. https://omanscience.com/en/articles/learning-to-leverage-compliance-a-policy-admittance-learning-framework-for-robotic-insertion
IEEE
C. Wang, M. Li, H. Dai, Z. Lin, S. Wei, and X. Yue, "Learning to Leverage Compliance: A Policy-Admittance Learning Framework for Robotic Insertion," https://omanscience.com/en/articles/learning-to-leverage-compliance-a-policy-admittance-learning-framework-for-robotic-insertion.