TOPReward

TOPReward / TOPReward

Public

TOPReward: Token Probabilities as Hidden Zero-Shot Rewards for Robotics

topreward.github.iowebpage
44
3
100% credibility
Found Mar 05, 2026 at 44 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

TOPReward lets you score robot performance on tasks using AI vision models that analyze video trajectories without needing labeled data.

How It Works

1
🔍 Discover TOPReward

You find this tool while reading about smart ways to score robot videos without manual work.

2
📥 Get it ready

Download the tool and prepare your computer with simple everyday tools like video player software.

3
📹 Gather robot videos

Choose videos of robots doing tasks, like opening doors or picking objects, from shared collections or your own.

4
🤖 Pick an AI helper

Select a smart AI vision expert to watch and judge the videos.

5
⚡ Launch the evaluation

With one click, the AI watches your robot videos and scores how well each task is going.

6
📊 Review the scores

See clear percentages or rewards showing robot progress on each task, saved neatly for you.

✅ Boost your robots

Use the scores to train better robots that learn tasks faster and more reliably.

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AI-Generated Review

What is TOPReward?

TOPReward extracts zero-shot rewards for robotics by computing token probabilities from vision-language models on video trajectories matching task instructions. In Python, it turns raw robot footage into scalable, annotation-free signals for learning and data filtering—think log-likelihood scores as hidden rewards without training reward models. Users get CLI scripts to predict on 50+ LeRobot datasets from Hugging Face, supporting models like Gemini, Qwen, and OpenAI.

Why is it gaining traction?

It skips expensive reward annotation by leveraging VLMs' built-in probabilities, outperforming baselines in recent robotics benchmarks. Extensible configs let you swap datasets, models, or prompts via simple overrides, plus dual modes for completion percentages (GVL) or direct instruction rewards. Devs love the quick-start bash runner and resume-from-checkpoint for batch jobs.

Who should use this?

Robotics ML engineers curating imitation learning datasets or tuning RL policies without custom rewards. Ideal for researchers prototyping zero-shot value functions on real-world manipulation tasks like Aloha or Berkeley datasets. Skip if you're not in robot trajectory evaluation.

Verdict

Worth forking for robotics reward experiments—solid README, MIT license, and arXiv paper make it legit despite 44 stars and 1.0% credibility score. Early-stage but production-ready for Hydra-savvy users; test coverage and extensibility shine, though scale to your own videos needs tweaks.

(198 words)

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