Leveraging Diverse Web Videos to Enable Zero-shot Robot Manipulation through Embodiment-Agnostic Interaction Plans

Core Concepts

A framework that leverages diverse web videos to learn embodiment-agnostic interaction plans, which can be combined with a small amount of robot-specific data to enable zero-shot robot manipulation across unseen tasks, objects, and scenes.

Abstract

The paper presents Track2Act, a framework for enabling zero-shot robot manipulation by leveraging large-scale web video data. The key insight is to factorize the manipulation policy into two components:

An embodiment-agnostic interaction plan that predicts how points in an image should move in future frames to achieve a specified goal. This interaction plan is learned from diverse web videos of humans and robots manipulating everyday objects, using a diffusion transformer-based model.

A residual policy that is trained with a small amount of robot-specific data to correct for errors in the open-loop execution of the predicted interaction plan.

The interaction plan is used to infer 3D rigid transforms of the object to be manipulated, which are then executed by the robot in an open-loop manner. The residual policy further refines this open-loop plan to enable closed-loop deployment.
The authors evaluate the approach on a range of real-world robot manipulation tasks, demonstrating strong generalization to unseen tasks, objects, and scenes. The results show that the combination of the embodiment-agnostic interaction plan and the residual policy enables zero-shot robot manipulation, outperforming baselines that rely on large amounts of in-domain robot data or test-time adaptation.

Stats

The track prediction model is trained on 400,000 video clips from diverse web sources, including EpicKitchens, YouTube, and robot datasets like RT1 and BridgeData.
The residual policy is trained with around 400 trajectories of the Spot robot manipulating everyday objects.

Quotes

"We seek to learn a generalizable goal-conditioned policy that enables zero-shot robot manipulation — interacting with unseen objects in novel scenes without test-time adaptation."
"Our insight to develop an in-the-wild manipulation strategy that is also zero-shot deployable is to factorize a manipulation policy into an interaction-plan that can leverage diverse large-scale video sources on the web of humans and robots manipulating everyday objects and a residual policy that requires a small amount of embodiment-specific robot interaction data."

Key Insights Distilled From

Track2Act: Predicting Point Tracks from Internet Videos enables Diverse Zero-shot Robot Manipulation

by Homanga Bhar... at arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.01527.pdf

Deeper Inquiries

How can the interaction plan prediction model be extended to handle long-horizon tasks that involve successive manipulations of multiple objects in a scene

To extend the interaction plan prediction model for long-horizon tasks involving multiple objects, several modifications can be considered. One approach is to incorporate a hierarchical structure in the prediction model, where the model can predict sub-goals or sub-tasks for each object manipulation step. By breaking down the long-horizon task into smaller, manageable sub-tasks, the model can predict interaction plans for each step, leading to the overall completion of the task. Additionally, introducing memory mechanisms or attention mechanisms can help the model remember past interactions and objects' states, enabling it to plan for successive manipulations effectively. Reinforcement learning techniques can also be employed to optimize the interaction plan over multiple steps, ensuring a coherent and efficient sequence of actions for complex tasks.

What are the potential limitations of the current approach in handling complex object interactions, such as deformable or articulated objects

The current approach may face limitations when handling complex object interactions, especially with deformable or articulated objects. Deformable objects, such as fabrics or soft materials, pose challenges in predicting accurate interaction plans due to their dynamic and unpredictable nature. The model may struggle to capture the deformations and movements of such objects accurately. Similarly, articulated objects with multiple moving parts can introduce complexities in predicting interaction plans, as the model needs to account for the interplay between different components. Additionally, the model may lack the ability to adapt to unforeseen deformations or articulations during manipulation, leading to suboptimal performance in such scenarios.

Could the framework be further generalized to learn from other modalities beyond just video, such as language or audio, to enable even more diverse and flexible zero-shot manipulation capabilities

To generalize the framework for learning from other modalities beyond video, such as language or audio, for zero-shot manipulation capabilities, a multimodal approach can be adopted. By incorporating language inputs describing the task or audio cues indicating specific actions, the model can learn to associate different modalities with manipulation actions. For example, language inputs can provide high-level task descriptions, while audio cues can offer additional context or timing information for actions. Multimodal fusion techniques, such as attention mechanisms or fusion networks, can be utilized to combine information from different modalities effectively. This extension can enhance the model's flexibility and adaptability to diverse input sources, enabling more comprehensive and versatile zero-shot manipulation capabilities.