Language-Conditioned Semantic Search-Based Policy for Robotic
  Manipulation Tasks
Jannik Sheikh Andrew Melnik Gora Chand Nandi Robert Haschke
Reinforcement learning and Imitation Learning approaches utilize policy
learning strategies that are difficult to generalize well with just a few
examples of a task. In this work, we propose a language-conditioned semantic
search-based method to produce an online search-based policy from the available
demonstration dataset of state-action trajectories. Here we directly acquire
actions from the most similar manipulation trajectories found in the dataset.
Our approach surpasses the performance of the baselines on the CALVIN benchmark
and exhibits strong zero-shot adaptation capabilities. This holds great
potential for expanding the use of our online search-based policy approach to
tasks typically addressed by Imitation Learning or Reinforcement Learning-based
policies.