Transformer-based deep imitation learning for dual-arm robot manipulation
This work addresses a domain-specific problem for robotics by improving dual-arm manipulation performance, though it is incremental as it adapts existing Transformer architectures to a known bottleneck.
The paper tackled the challenge of applying deep imitation learning to dual-arm robot manipulation by using a Transformer-based self-attention mechanism to reduce distractions from increased state dimensions, resulting in improved manipulation performance compared to baseline methods without self-attention.
Deep imitation learning is promising for solving dexterous manipulation tasks because it does not require an environment model and pre-programmed robot behavior. However, its application to dual-arm manipulation tasks remains challenging. In a dual-arm manipulation setup, the increased number of state dimensions caused by the additional robot manipulators causes distractions and results in poor performance of the neural networks. We address this issue using a self-attention mechanism that computes dependencies between elements in a sequential input and focuses on important elements. A Transformer, a variant of self-attention architecture, is applied to deep imitation learning to solve dual-arm manipulation tasks in the real world. The proposed method has been tested on dual-arm manipulation tasks using a real robot. The experimental results demonstrated that the Transformer-based deep imitation learning architecture can attend to the important features among the sensory inputs, therefore reducing distractions and improving manipulation performance when compared with the baseline architecture without the self-attention mechanisms. Data from this and related works are available at: https://sites.google.com/view/multi-task-fine.