Using deep reinforcement learning and 'proprioception,' the two robotic limbs can pick up extremely fragile objects.
What makes the team’s advancements so promising is its leveraging of two robotic arms, versus aas usually seen in most tactile robotic projects. Despite doubling the number of limbs, however, training only takes just a few hours.
To accomplish this, researchers first train their AI in a simulation environment, then apply the finalized Bi-Touch system to their physical robot arms.“With our Bi-Touch system, we can easily train AI agents in a virtual world within a couple of hours to achieve bimanual tasks that are tailored towards the touch,” Lin continued. “And more importantly, we can directly apply these agents from the virtual world to the real world without further training.
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