Inference

Running a trained model forward on new input to produce an output, without updating its parameters.

Inference means using an already-trained model: feed in new data, run one forward pass to get an output, without computing gradients or updating parameters — the counterpart to “training.” In Chinese, 推理 covers both “inference” and “reasoning” (a model thinking step by step), so papers need to be read in context to tell which one is meant. For robots, inference speed determines whether control can keep up: the π0 paper reports that processing three camera views and generating one action chunk takes about 73 milliseconds on a single RTX 4090. If inference is too slow, the robot stalls between action segments, which is why techniques such as action chunking, asynchronous inference, quantization, and TensorRT acceleration exist. Inference can run on the robot itself, or on a remote server with results sent back over the network, which adds network latency to the total.

ExampleWhen π0 controls a robot at 50 Hz, it runs inference once every 0.5 seconds to produce one action chunk, then executes all 25 steps of that chunk before running inference again for the next one.

Related

Reasoning · Inference Latency · Action Chunking · Asynchronous Inference · Inference Deployment · Post-Training Quantization

Sources

What is AI inference? (IBM)
π0: A Vision-Language-Action Flow Model for General Robot Control (arXiv:2410.24164)

As of

2024-10