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How to supervise actions with next-latent prediction with Robo TTT

Enable predict_next_latent so action generation is supervised by next-state representation prediction.

Short answer: Install robo-ttt; then set predict_next_latent = True and designate state_arg; Tune next_latent_predictor_kwargs for loss weighting. All steps.

Who it is forA researcher testing forward dynamics without pixel reconstruction.

The problemPixel reconstruction losses are expensive.

The outcomeA policy that also predicts the next latent state during training and rollout.

What you need first

  • Python with PyTorch

Steps

  1. Install robo-ttt
  2. Set predict_next_latent = True and designate state_arg
  3. Tune next_latent_predictor_kwargs for loss weighting
  4. Request the predicted next latent during rollout sampling

Prompt for your agent

In robo_ttt, enable predict_next_latent with a state_arg, train for a few steps on dummy data, and report the dynamics loss. Confirm rollout returns the predicted next latent.

Caveat. This option follows Xu et al., 2026, which is separate from the RoboTTT paper.

Use this in your AI agent

Ask your agent

Using the Deduped MCP, get the setup steps for lucidrains/robo_ttt and "Supervise actions with next-latent prediction"

Add Deduped to Claude Code once

claude mcp add --transport http deduped https://dedupedai.com/mcp

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