aiqbench analysis
UniReflex introduces a plug-and-play framework that enables frozen generative policies in robotics to perform closed-loop force regulation without retraining the main network backbone. By decoupling active force exertion from interaction response through fast reflex networks, this method significantly improves contact stability and reduces computational latency compared to joint training strategies. The approach represents a meaningful advancement for embodied AI systems requiring precise impedance control during physical interactions with dynamic environments.
UniReflex: Plug-and-Play Force Control for Pretrained Generative Policies via Fast-Slow Reflex
arXiv:2608.17432v1 Announce Type: new Abstract: Generative imitation learning policies excel at trajectory planning but lack closed-loop force regulation, while directly incorporating force modalities often requires redesigning or retraining the network. We present UniReflex, a universal plug-and-play framework that equips frozen generative policies with variable impedance control (VIC) for contact regulation, guided by force-direction intent collected during demonstration, without further slow-backbone fine-tuning. By non-invasively intercepting deep latent representations from the action head, UniReflex drives a fast reflex network that decouples active force exertion from external interaction response. This scheme predicts normalized anisotropic stiffness directions for directional compliance allocation. Furthermore, UniReflex integrates an adaptive gating mechanism that enables seamless transitions between position-dominant planning and force-dominant execution. Real-world bimanual experiments demonstrate that UniReflex significantly improves contact stability and success rates while preserving original position accuracy. Our approach achieves 25-66x lower per-step backward latency relative to joint training strategies on the evaluated backbones.
Abstract:Generative imitation learning policies excel at trajectory planning but lack closed-loop force regulation, while directly incorporating force modalities often requires redesigning or retraining the network. We present UniReflex, a universal plug-and-play framework that equips frozen generative policies with variable impedance control (VIC) for contact regulation, guided by force-direction intent collected during demonstration, without further slow-backbone fine-tuning. By non-invasively intercepting deep latent representations from the action head, UniReflex drives a fast reflex network that decouples active force exertion from external interaction response. This scheme predicts normalized anisotropic stiffness directions for directional compliance allocation. Furthermore, UniReflex integrates an adaptive gating mechanism that enables seamless transitions between position-dominant planning and force-dominant execution. Real-world bimanual experiments demonstrate that UniReflex significantly improves contact stability and success rates while preserving original position accuracy. Our approach achieves 25-66x lower per-step backward latency relative to joint training strategies on the evaluated backbones.
Robotics (cs.RO)
arXiv:2608.17432 [cs.RO]
(or arXiv:2608.17432v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2608.17432 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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