Panasonic Holdings Corporation and Panasonic R&D Company of America, with researchers from the University of California, Berkeley, NVIDIA, Stanford University, Sapienza University of Rome, and ItalAI, have developed "Tactile-Reactive Dexterous Manipulation (T-Rex)," a robot manipulation AI that performs dexterous bimanual tasks while responding instantly to fingertip touch. T-Rex integrates fingertip tactile information into a VLA model and corrects actions in real time as contact conditions change. In real-world evaluations across 12 tasks requiring delicate force control and handling of soft objects, T-Rex achieved an average success rate of 65%, outperforming existing methods by 30 percentage points. The results will be presented at the Conference on Robot Learning (CoRL) in Austin, United States, from November 9 to 12, 2026. T-Rex combines low-frequency action generation based on vision and language at approximately 5 Hz with high-speed action correction driven by tactile feedback at approximately 20 Hz. For training data, the team combined more than 200 types of everyday objects with 22 types of basic actions to collect over 7,700 episodes—a total of 100 hours—of bimanual robot manipulation data, with each episode synchronously recording video from head and wrist cameras, robot states and actions, force and deformation information from tactile sensors on 10 fingertips, and natural-language instructions.