The Speedup Paradox: Rethinking Inference Speed-Quality Trade-off in Embodied Tasks

arXiv:2606.28529v1 Announce Type: new Abstract: Embodied foundation models have recently been widely used to improve robot generalization and task success rates. Previous works apply lossy efficient-inference techniques such as quantization, pruning, and asynchronous inference, accepting small action quality degradation in exchange for lower per-step computation cost and inter-action latency. However, unlike traditional static ML tasks, embodied tasks involve repeated interaction with the enviro...