TacMind: Benchmarking and Modeling Tactile Interaction States for Robotic Manipulation
TacMind: Benchmarking and Modeling Tactile Interaction States for Robotic Manipulation
This paper presents TacMind, a benchmark in simulation comprising 128 tasks organized into Precise, Recognition, and Dynamic evaluation regimes, along with controlled static/dynamic variants and 12 perturbation settings to assess robustness. The authors propose an action-conditioned latent predictive world model that integrates tactile history to learn a tactile state via prediction of future tactile latents, using this representation to condition action generation and refine candidates through predictive tactile reflection. Experiments demonstrate consistent gains from incorporating tactile information across all evaluated regimes.
The authors introduce a simulation-based benchmark with 128 tasks across Precise, Recognition, and Dynamic regimes to evaluate tactile sensing for robotic manipulation, proposing an action-conditioned latent predictive world model that integrates tactile history to refine candidate actions.
Source: arXiv Robotics — research abstracts · Read original article ↗
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tributes under different interaction conditions. In this work, we develop a benchmark in simulation comprising 128 tasks organized around three complementary evaluation regimes: Precise, Recognition, and Dynamic. The benchmark further includes controlled static and dynamic task variants and 12 perturbation settings for evaluating robustness under changes in visual and physical conditions. These settings motivate stud
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nd uses predicted future tactile information to refine candidate actions through predictive tactile reflection. Experiments on the proposed benchmark show consistent gains from incorporating tactile information across the evaluated regimes. Project Page: https://tacmind-bench.github.io/
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Source：arXiv Robotics — research abstracts · arxiv.org