An Empowerment-based Solution to Robotic Manipulation Tasks with Sparse Rewards
This addresses the challenge of sparse rewards in reinforcement learning for robotic manipulation, potentially accelerating skill acquisition for more complex tasks.
The paper tackles the problem of robotic manipulation learning with sparse rewards by proposing an intrinsic motivation approach that integrates empowerment and curiosity, showing superior performance compared to state-of-the-art methods in empirical tests.
In order to provide adaptive and user-friendly solutions to robotic manipulation, it is important that the agent can learn to accomplish tasks even if they are only provided with very sparse instruction signals. To address the issues reinforcement learning algorithms face when task rewards are sparse, this paper proposes an intrinsic motivation approach that can be easily integrated into any standard reinforcement learning algorithm and can allow robotic manipulators to learn useful manipulation skills with only sparse extrinsic rewards. Through integrating and balancing empowerment and curiosity, this approach shows superior performance compared to other state-of-the-art intrinsic exploration approaches during extensive empirical testing. Qualitative analysis also shows that when combined with diversity-driven intrinsic motivations, this approach can help manipulators learn a set of diverse skills which could potentially be applied to other more complicated manipulation tasks and accelerate their learning process.