TacStyle: Personalizing Tactile Robot Policies using Structured Behavior Representations
TacStyle proposes a novel approach that learns a structured latent representation of user preferences and uses a foundation model to interpret this space to select desired behaviors, enabling fine-grained control over robot behavior with significantly fewer preference labels. Experiments in simulation and real-world demonstrate more precise adaptation.
[2606.14862] TacStyle: Personalizing Tactile Robot Policies using Structured Behavior Representations
[Submitted on 12 Jun 2026]
Title:TacStyle: Personalizing Tactile Robot Policies using Structured Behavior Representations
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Abstract:Robotic systems that assist humans should be capable of adapting their behaviors to individual user preferences. For instance, users may want a robot arm to adjust the amount of force it applies while folding their laundry or cleaning furniture. Natural language provides an intuitive way for humans to communicate such preferences. Recent progress in language-conditioned robot policies has shown that robots can successfully use language prompts to determine what task to perform. However, extending the same approach to realize how the task should be performed requires detailed labels describing the preferences or styles of trajectories in the task data. Not only is collecting such annotations challenging, but conditioning directly on these labels may also fail to provide fine-grained control over a continuous range of behaviors. For example, it can be difficult to convey the exact force that a robot must apply through abstract instructions like "apply a bit more pressure than before". Therefore, in this work, we propose using language to reason over preferred behaviors instead of directly generating them. We first learn a structured latent representation that organizes user preferences according to differences in the corresponding trajectories. Then, given a preference prompt, we use a foundation model to interpret this latent space and choose a value that produces the desired behavior. Through both simulation and real-world experiments, we show that selecting robot behaviors from an intuitively structured latent space enables more precise adaptation to user preferences while requiring significantly fewer preference labels than language-conditioned policies.
Comments: 14 pages, 5 figures
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2606.14862 [cs.RO]
(or arXiv:2606.14862v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.14862
arXiv-issued DOI via DataCite
Submission history
From: Heramb Nemlekar [view email] [v1] Fri, 12 Jun 2026 18:04:28 UTC (5,273 KB)
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