Action Chunking with Transformers (ACT)

Links

• https://tonyzhaozh.github.io/aloha/
• paper https://arxiv.org/pdf/2304.13705

Resources

• https://radekosmulski.com/how-to-train-your-robot-with-a-transformer/

So what is action chunking? Action chunking is a technique in robotics and imitation learning where, instead of predicting and executing one action at a time, a robot forecasts and carries out a sequence of actions—referred to as a “chunk”—based on its current observation.

Benefits of action chunking (reference blog):

• Allows your model to control your robot at a much higher frequency given a large model
  • You of course don’t get the reactivity, but that’s a bonus
• Better temporal consistency without having to do Proprioceptive History

“Generally, the more information is contained in the observation space, the more likely causal confusion will happen for imitation learning.”

Related

• Real-Time Execution of Action Chunking Flow Policies