AT-VLA proposes adaptive tactile injection and a dual-stream tactile reaction mechanism to enhance VLA models for contact-rich robotic manipulation with real-time responses.
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The paper provides a task-driven benchmark comparing visual, acoustic, magnetic, and resistive tactile sensors on three manipulation tasks and concludes that sensor utility depends on modality, material friction, and task specifics.
A hierarchical tactile-aware policy combines human-demonstration training for contact cue prediction with sim-to-real reinforcement learning to improve quadrupedal loco-manipulation performance by 28.54% over vision baselines on contact-rich tasks.
A position paper proposes decomposing affective robotic touch into multiple specialized deep learning models for better social human-robot interaction.
citing papers explorer
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AT-VLA: Adaptive Tactile Injection for Enhanced Feedback Reaction in Vision-Language-Action Models
AT-VLA proposes adaptive tactile injection and a dual-stream tactile reaction mechanism to enhance VLA models for contact-rich robotic manipulation with real-time responses.
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TacO: Benchmarking Tactile Sensors for Object Manipulation
The paper provides a task-driven benchmark comparing visual, acoustic, magnetic, and resistive tactile sensors on three manipulation tasks and concludes that sensor utility depends on modality, material friction, and task specifics.
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Learning Tactile-Aware Quadrupedal Loco-Manipulation Policies
A hierarchical tactile-aware policy combines human-demonstration training for contact cue prediction with sim-to-real reinforcement learning to improve quadrupedal loco-manipulation performance by 28.54% over vision baselines on contact-rich tasks.
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Robotic Affection -- Opportunities of AI-based haptic interactions to improve social robotic touch through a multi-deep-learning approach
A position paper proposes decomposing affective robotic touch into multiple specialized deep learning models for better social human-robot interaction.