Researchers at Stanford Propose TRANSIC: A Human-in-the-Loop Method to Handle the Sim-to-Real Transfer of Policies for Contact-Rich Manipulation Tasks

Learning in simulation and applying the learned policy to the real world is a potential approach to enable generalist robots, and solve complex decision-making tasks. However, the challenge to this approach is to address simulation-to-reality (sim-to-real) gaps. Also, a huge amount of data is needed while learning to solve these tasks, and the load of […]

The post Researchers at Stanford Propose TRANSIC: A Human-in-the-Loop Method to Handle the Sim-to-Real Transfer of Policies for Contact-Rich Manipulation Tasks appeared first on …

ai paper summary ai shorts applications artificial intelligence challenge decision editors pick however human loop making manipulation policies policy potential reality researchers robotics robots sim sim-to-real simulation solve staff stanford tasks tech news technology transfer world