VLBiMan++: Expanding Generalization in One-Shot Bimanual Robotic Manipulation

Overview

VLBiMan++ represents an extended framework designed to broaden the generalization capabilities of vision-language anchored one-shot bimanual robotic manipulation. The system addresses the necessity for a reusable task prior that can function across a variety of tasks, objects, scenes, embodiments, and execution conditions, aiming to mitigate the substantial costs associated with extensive teleoperated demonstrations and policy retraining. Starting from a single human demonstration, VLBiMan++ implements a task-aware decomposition process to identify reusable and adaptable skill components. Subsequently, it employs vision-language grounded geometric adaptation to transfer these identified skills to novel configurations without requiring retraining.

Research Context

The development of generalizable bimanual robotic manipulation faces challenges related to the persistence of task priors across varied operational parameters. Traditional approaches often incur significant costs due to the need for large-scale teleoperated demonstrations and subsequent retraining of policies for each new scenario. The work on VLBiMan++ seeks to address these limitations by focusing on a one-shot learning paradigm, wherein a single human demonstration serves as the basis for skill acquisition and adaptation.

Approach

The methodological foundation of VLBiMan++ involves several key steps:

• Task-aware decomposition: From a single human demonstration, the system identifies and separates reusable and adaptable skill components. This process is crucial for breaking down complex bimanual tasks into manageable units that can be applied in different contexts.
• Vision-language grounded geometric adaptation: Identified skill components are transferred to novel configurations. This adaptation relies on integrating visual and linguistic information to adjust geometric parameters, enabling skill application without requiring policy retraining.

Building upon these foundational elements, VLBiMan++ incorporates additional mechanisms to enhance generalization:

• Object-state-aware adaptation: This mechanism accommodates changes that extend beyond simple rigid 6-DoF pose variations, allowing the system to handle more complex object interactions.
• Lightweight trajectory optimization: Implemented to preserve reliable bimanual coordination even when adapting to changes in object states.

This integrated approach aims to systematically expand the framework's generalization boundaries.

Findings

VLBiMan++ demonstrates enhanced generalization across five distinct dimensions:

• Task generalization: Achieved through the composition of diverse and long-horizon skills.
• Object generalization: Extended to include unseen object categories, objects with varying geometries, and more complex articulated or deformable objects.
• Scene generalization: The framework operates effectively under conditions of clutter, occlusion, and dynamic interference within the environment.
• Embodiment generalization: Demonstrated across heterogeneous dual-arm robotic platforms, indicating adaptability to different robotic hardware configurations.
• Deployment generalization: Supported through prolonged closed-loop execution, even when subjected to repeated external perturbations.

Extensive real-world experiments indicated that VLBiMan++ maintained strong task success rates and exhibited robust adaptation capabilities across these increasingly challenging settings.

Why This Matters

The advancement presented by VLBiMan++ transitions one-shot bimanual manipulation from demonstrating isolated transferability toward establishing a more systematic and scalable framework for generalization. This approach offers a pathway to reduce the prohibitive costs associated with extensive data collection and retraining in robotic manipulation tasks.