TACTILE-VLA：激活VLA模型的物理知识以实现触觉泛化（清华大学最新）

Core Insights - The article discusses the development of Tactile-VLA, a model that integrates visual, language, action, and tactile perception to enhance robotic capabilities in contact-intensive tasks [2][6]. Group 1: Background and Core Issues - Visual-language-action (VLA) models have strong semantic understanding and cross-modal generalization capabilities, but they struggle in contact-intensive scenarios due to a lack of tactile perception [2][6]. - Tactile perception provides critical feedback in physical interactions, such as friction and material properties, which are essential for tasks requiring fine motor control [2][6]. Group 2: Key Innovations and Research Goals - The core finding is that VLA models contain prior knowledge of physical interactions, which can be activated by connecting this knowledge with tactile sensors, enabling zero-shot generalization in contact-intensive tasks [6][7]. - Tactile-VLA framework introduces tactile perception as a primary modality, allowing for direct mapping from abstract semantics to physical force control [7]. - The mixed position-force controller innovatively converts force targets into position adjustment commands, addressing the challenge of coordinating position and force control [7]. Group 3: Architecture and Mechanisms - Tactile-VLA's architecture includes four key modules: instruction adherence to tactile cues, application of tactile-related common sense, adaptive reasoning through tactile feedback, and a multi-modal encoder for unified token representation [12][13]. - The mixed position-force control mechanism ensures precision in position while allowing for fine-tuned force adjustments during contact tasks [13]. - The Tactile-VLA-CoT variant incorporates a chain of thought (CoT) reasoning mechanism, enabling robots to analyze failure causes based on tactile feedback and autonomously adjust strategies [13][14]. Group 4: Experimental Validation and Results - Three experimental setups were designed to validate Tactile-VLA's capabilities in instruction adherence, common sense application, and adaptive reasoning [17]. - In the instruction adherence experiment, Tactile-VLA achieved a success rate of 35% in USB tasks and 90% in charger tasks, significantly outperforming baseline models [21][22]. - The common sense application experiment demonstrated Tactile-VLA's ability to adjust interaction forces based on object properties, achieving success rates of 90%-100% for known objects and 80%-100% for unknown objects [27]. - The adaptive reasoning experiment showed that Tactile-VLA-CoT could successfully complete a blackboard task with an 80% success rate, demonstrating its problem-solving capabilities through reasoning [33].