3PoinTr: 3D Point Tracks for Robot Manipulation Pretraining from Casual Videos

TL;DR

3PoinTr introduces a transformer-based method for pretraining robot manipulation policies from casual human videos by predicting 3D point tracks, enabling robust generalization with minimal demonstrations.

Contribution

The paper presents a novel approach using 3D point track prediction with a transformer architecture for embodiment-agnostic robot policy pretraining from unconstrained human videos.

Findings

Achieves robust spatial generalization with only 20 demonstrations.

Outperforms existing behavior cloning and pretraining methods.

Produces more accurate 3D point tracks than baseline models.

Abstract

Data-efficient training of robust robot policies is the key to unlocking automation in a wide array of novel tasks. Current systems require large volumes of demonstrations to achieve robustness, which is impractical in many applications. Learning policies directly from human videos is a promising alternative that removes teleoperation costs, but it shifts the challenge toward overcoming the embodiment gap (differences in kinematics and strategies between robots and humans), often requiring restrictive and carefully choreographed human motions. We propose 3PoinTr, a method for pretraining robot policies from casual and unconstrained human videos, enabling learning from motions natural for humans. 3PoinTr uses a transformer architecture to predict 3D point tracks as an intermediate embodiment-agnostic representation. 3D point tracks encode goal specifications, scene geometry, and spatiotemporal relationships. We use a Perceiver IO architecture to extract a compact representation for sample-efficient behavior cloning, even when point tracks violate downstream embodiment-specific constraints. We conduct thorough evaluation on simulated and real-world tasks, and find that 3PoinTr achieves robust spatial generalization on diverse categories of manipulation tasks with only 20 action-labeled robot demonstrations. 3PoinTr outperforms the baselines, including behavior cloning methods, as well as prior methods for pretraining from human videos. We also provide evaluations of 3PoinTr's 3D point track predictions compared to an existing point track prediction baseline. We find that 3PoinTr produces more accurate and higher quality point tracks due to a lightweight yet expressive architecture built on a single transformer, in addition to a training formulation that preserves supervision of partially occluded points. Project page: https://adamhung60.github.io/3PoinTr/.