While solving complex manipulation tasks, manipulation policies often need to
learn a set of diverse skills to accomplish these tasks. The set of skills is
often quite multimodal – each one may have a quite distinct distribution of
actions and states. Standard deep policy-learning algorithms often model
policies as deep neural networks with a single output head (deterministic or
stochastic). This structure requires the network to learn to switch between
modes internally, which can lead to lower sample efficiency and poor
performance. In this paper we explore a simple structure which is conducive to
skill learning required for so many of the manipulation tasks. Specifically, we
propose a policy architecture that sequentially executes different action heads
for fixed durations, enabling the learning of primitive skills such as reaching
and grasping. Our empirical evaluation on the Metaworld tasks reveals that this
simple structure outperforms standard policy learning methods, highlighting its
potential for improved skill acquisition.
In this article, the authors discuss the challenges of complex manipulation tasks and the need for learning a diverse set of skills to accomplish these tasks. They recognize that this set of skills is often multidimensional, with each skill having a distinct distribution of actions and states.
The authors point out that standard deep policy-learning algorithms typically model policies as deep neural networks with a single output head. However, this structure can hinder performance and sample efficiency because the network needs to learn to switch between different modes internally.
To address this issue, the authors propose a policy architecture that sequentially executes different action heads for fixed durations. This sequential execution enables the learning of primitive skills such as reaching and grasping. By incorporating this structure into their policy-learning algorithm, they aim to improve skill acquisition in manipulation tasks.
The authors then conduct an empirical evaluation on the Metaworld tasks to assess the effectiveness of their proposed approach. The results indicate that this simple structure outperforms standard policy-learning methods, suggesting its potential for enhancing skill acquisition.
This research has several interdisciplinary implications. Firstly, it combines concepts from deep learning and robotics to address the challenges of manipulation tasks. Secondly, it highlights the importance of considering the multimodal nature of skills in policy-learning algorithms. By incorporating different action heads, the proposed architecture can better capture the diversity of skills required in manipulation tasks.
In terms of future directions, further research can explore ways to enhance the sequential execution of action heads. This can involve optimizing the duration of each head’s execution or dynamically adapting the durations based on task requirements. Additionally, investigating the transferability of learned skills across different manipulation tasks would be valuable in developing more generalized learning algorithms.