SoftNash: Entropy-Regularized Nash Games for Non-Fighting Virtual Fixtures
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arXiv
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| Format: | Preprint |
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2025
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| author | Inui, Tai Ryu, Jee-Hwan |
| author_facet | Inui, Tai Ryu, Jee-Hwan |
| contents | Virtual fixtures (VFs) improve precision in teleoperation but often ``fight'' the user, inflating mental workload and eroding the sense of agency. We propose Soft-Nash Virtual Fixtures, a game-theoretic shared-control policy that softens the classic two-player linear-quadratic (LQ) Nash solution by inflating the fixture's effort weight with a single, interpretable scalar parameter $τ$. This yields a continuous dial on controller assertiveness: $τ=0$ recovers a hard, performance-focused Nash / virtual fixture controller, while larger $τ$ reduce gains and pushback, yet preserve the equilibrium structure and continuity of closed-loop stability. We derive Soft-Nash from both a KL-regularized trust-region and a maximum-entropy viewpoint, obtaining a closed-form robot best response that shrinks authority and aligns the fixture with the operator's input as $τ$ grows. We implement Soft-Nash on a 6-DoF haptic device in 3D tracking task ($n=12$). Moderate softness ($τ\approx 1-3$, especially $τ=2$) maintains tracking error statistically indistinguishable from a tuned classic VF while sharply reducing controller-user conflict, lowering NASA-TLX workload, and increasing Sense of Agency (SoAS). A composite BalancedScore that combines normalized accuracy and non-fighting behavior peaks near $τ=2-3$. These results show that a one-parameter Soft-Nash policy can preserve accuracy while improving comfort and perceived agency, providing a practical and interpretable pathway to personalized shared control in haptics and teleoperation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_22087 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SoftNash: Entropy-Regularized Nash Games for Non-Fighting Virtual Fixtures Inui, Tai Ryu, Jee-Hwan Robotics Human-Computer Interaction Virtual fixtures (VFs) improve precision in teleoperation but often ``fight'' the user, inflating mental workload and eroding the sense of agency. We propose Soft-Nash Virtual Fixtures, a game-theoretic shared-control policy that softens the classic two-player linear-quadratic (LQ) Nash solution by inflating the fixture's effort weight with a single, interpretable scalar parameter $τ$. This yields a continuous dial on controller assertiveness: $τ=0$ recovers a hard, performance-focused Nash / virtual fixture controller, while larger $τ$ reduce gains and pushback, yet preserve the equilibrium structure and continuity of closed-loop stability. We derive Soft-Nash from both a KL-regularized trust-region and a maximum-entropy viewpoint, obtaining a closed-form robot best response that shrinks authority and aligns the fixture with the operator's input as $τ$ grows. We implement Soft-Nash on a 6-DoF haptic device in 3D tracking task ($n=12$). Moderate softness ($τ\approx 1-3$, especially $τ=2$) maintains tracking error statistically indistinguishable from a tuned classic VF while sharply reducing controller-user conflict, lowering NASA-TLX workload, and increasing Sense of Agency (SoAS). A composite BalancedScore that combines normalized accuracy and non-fighting behavior peaks near $τ=2-3$. These results show that a one-parameter Soft-Nash policy can preserve accuracy while improving comfort and perceived agency, providing a practical and interpretable pathway to personalized shared control in haptics and teleoperation. |
| title | SoftNash: Entropy-Regularized Nash Games for Non-Fighting Virtual Fixtures |
| topic | Robotics Human-Computer Interaction |
| url | https://arxiv.org/abs/2511.22087 |