Measuring the Stability Assumption Behind Action Chunking
Quick summary
arXiv:2610.01626v1 Announce Type: new Abstract: Action chunking improves the performance of policies learned by behavioural cloning, and several mechanisms have been proposed to explain why, including temporal consistency, horizon reduction, representation learning, and reduced error compounding. We instead study what happens to an action error once it enters the system. At each state, we inject a small action error and measure how fast it grows or shrinks under two execution regimes: open-loop, where the rest of the chunk is replayed without replanning, and closed-loop, where the policy repla
Key takeaways
- arXiv:2610.01626v1 Announce Type: new Abstract: Action chunking improves the performance of policies learned by behavioural cloning, and several mechanisms have been proposed to explain why, including temporal consistency, horizon reduction, representation learning, and reduced error compounding.
- We instead study what happens to an action error once it enters the system.
- At each state, we inject a small action error and measure how fast it grows or shrinks under two execution regimes: open-loop, where the rest of the chunk is replayed without replanning, and closed-loop, where the policy repla
Why it matters
The significance is not only the legal text but how it changes product design. Decisions around “Measuring the Stability Assumption Behind Action Chunking” may reshape data collection, model training, output accountability and market access.
Member comments