MP3: Movement Primitive-Based (Re-)Planning Policy

Understand

We introduce a novel deep reinforcement learning (RL) approach called Movement Primitive-based Planning Policy (MP3).

• By integrating movement primitives (MPs) into the deep RL framework, MP3 enables the generation of smooth trajectories throughout the whole learning process while effectively learning from sparse and non-Markovian rewards.
• Additionally, MP3 maintains the capability to adapt to changes in the environment during execution.
• Although many early successes in robot RL have been achieved by combining RL with MPs, these approaches are often limited to learning single stroke-based motions, lacking the ability to adapt to task variations or adjust motions during execution.