Enhancing Robotic Contact-Rich Manipulation Through Observable Passive Compliance

Small pose errors in contact-rich manipulation can cause rapid force buildup, restricting exploration and complicating demonstration collection and policy learning. This study presents observable passive compliance, which combines mechanical accommodation with deformation-based contact observation. A six-degree-of-freedom passive Stewart-platform wrist transforms brief contact transients into extended, measurable deformation, providing a “contact amplification” mechanism. Visual measurements of wrist deformation are supplied directly to an imitation-learning policy, while a learned empirical deformation envelope supports state monitoring. Force-admittance compliance (F-AC), unobserved passive compliance (Com-NoOb), and observable passive compliance (Com-Ob) are evaluated within a common learning framework using matched demonstration budgets. In a controlled probe experiment, the passive wrist reduced the maximum contact-force loading rate by 31.6%. In board and USB insertion, Com-Ob achieved significantly higher success rates than Com-NoOb with 30 and 50 demonstrations after Holm correction. With 50 demonstrations, Com-Ob succeeded in all 25 rollouts for each task, compared with success rates of 44% and 60% for Com-NoOb, respectively. Both passive configurations shortened successful demonstration and execution times relative to F-AC. An additional bolt–nut engagement experiment achieved 16 successful Com-Ob rollouts out of 25. These findings support the complementary mechanical and informational benefits of observable passive compliance. Wrist deformation provides useful cues about local contact constraints, and the gains observed with larger demonstration sets suggest that these cues help policies use demonstrated contact experience more effectively.

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Publication Details

Journal
Machines
Published
2026-10-12
DOI
https://doi.org/10.3390/machines14101184
Primary Topic
Robot Manipulation and Learning
Type
article
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article

Enhancing Robotic Contact-Rich Manipulation Through Observable Passive Compliance

Dianjin Zhang, Michael Yu Wang, Houde Liu, Mingda Zhang
Machines
Robot Manipulation and Learning
article

Enhancing Robotic Contact-Rich Manipulation Through Observable Passive Compliance

Dianjin Zhang, Michael Yu Wang, Houde Liu, Mingda Zhang
article en

Abstract

Small pose errors in contact-rich manipulation can cause rapid force buildup, restricting exploration and complicating demonstration collection and policy learning. This study presents observable passive compliance, which combines mechanical accommodation with deformation-based contact observation. A six-degree-of-freedom passive Stewart-platform wrist transforms brief contact transients into extended, measurable deformation, providing a “contact amplification” mechanism. Visual measurements of wrist deformation are supplied directly to an imitation-learning policy, while a learned empirical deformation envelope supports state monitoring. Force-admittance compliance (F-AC), unobserved passive compliance (Com-NoOb), and observable passive compliance (Com-Ob) are evaluated within a common learning framework using matched demonstration budgets. In a controlled probe experiment, the passive wrist reduced the maximum contact-force loading rate by 31.6%. In board and USB insertion, Com-Ob achieved significantly higher success rates than Com-NoOb with 30 and 50 demonstrations after Holm correction. With 50 demonstrations, Com-Ob succeeded in all 25 rollouts for each task, compared with success rates of 44% and 60% for Com-NoOb, respectively. Both passive configurations shortened successful demonstration and execution times relative to F-AC. An additional bolt–nut engagement experiment achieved 16 successful Com-Ob rollouts out of 25. These findings support the complementary mechanical and informational benefits of observable passive compliance. Wrist deformation provides useful cues about local contact constraints, and the gains observed with larger demonstration sets suggest that these cues help policies use demonstrated contact experience more effectively.

MachinesVol. 14(10)
Great Bay University, Tsinghua Shenzhen International Graduate School (CN), Xi'an Jiaotong University (CN), Tsinghua University (CN)
Openalex Percentile: Top 16%
Robot Manipulation and Learning
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