Robotic Arm Manipulation with Inverse Reinforcement Learning & TD-MPC

Research on learning from demonstrations is booming because it allows robots to quickly acquire new skills. In inverse reinforcement learning (IRL), for example, demonstrations might assist in a number of ways by having the robot attempt to deduce the objectives or reward from the human demonstrator. The majority of IRL techniques call for expensive to obtain demonstrations that link action and state measurements. With the use of visual examples, we move closer to model-based inverse reinforcement learning for basic object manipulation tasks. It is believed that model-based IRL techniques are more sample-efficient and have the potential to facilitate generalization [1]. However, their model-free equivalents have had greater success so far in robotics applications with unknown dynamics in the actual world [13, 3, 7]. Model-based IRL still faces the following significant obstacles: An inner and an outer optimization step are the two nested optimization issues that make up model-based inverse reinforcement learning.