Imitation Learning on Bimanual Robots

Bimanual robot manipulation, involving the coordinated control of two robot arms, holds great promise for enhancing the dexterity and efficiency of robotic systems across a wide range of applications, from manufacturing and healthcare to household chores and logistics. However, enabling robots to perform complex bimanual tasks with the same level of skill and adaptability as humans remains a challenging problem. The control of a bimanual robot can be tackled through various methods like inverse dynamic controller or reinforcement learning, but each of these methods have their own problems. Inverse dynamic controller cannot adapt to a changing environment, whereas Reinforcement learning is computationally intensive and may require weeks of training for even simple tasks, and reward formulation for Reinforcement Learning is often challenging and is still an open research topic. Imitation learning, leverages human demonstrations to enable robots to acquire the skills necessary for complex tasks and it can be highly sample-efficient and reduces exploration. Given the advantages of Imitation learning we want to explore the application of imitation learning techniques to bridge the gap between human expertise and robotic dexterity in the context of bimanual manipulation. In this thesis, an examination of the Implicit Behavioral Cloning imitation learning algorithm is conducted. Implicit behavioral cloning aims to capture the fundamental behavior or policy of the expert by utilizing energy-based models, which frequently demonstrate superior performance when compared to explicit behavior cloning policies. The assessment encompasses an investigation of the impact of expert demonstrations' quality on the efficacy of the acquired policies. Furthermore, computational and performance metrics of diverse training and inference techniques for energy-based models are compared.