PoseLess

Key features

Depth-Free Vision-to-Joint Control: PoseLess directly maps 2D monocular images to robot joint angles without requiring depth information.
Eliminates Explicit Pose Estimation: The framework bypasses the traditional step of estimating 3D pose or keypoints. This reduces error propagation from multi-stage processing.
Leverages Vision-Language Models (VLMs): PoseLess utilizes VLMs (e.g., Qwen 2.5 3B Instruct) to project visual inputs and decode them into joint angles. VLMs enable robust, morphology-agnostic feature extraction.
Synthetic Data Training: The model is trained on a large-scale synthetic dataset generated through randomized joint configurations and domain randomization of visual features. This eliminates the need for costly and labor-intensive real-world labeled data.
Cross-Morphology Generalization: PoseLess demonstrates the ability to transfer control policies learned from robotic hand data to real human hands.
Robustness to Real-World Variations: Training on synthetic data with domain randomization ensures adaptability to real-world variations.
Low-Latency Control: The direct image mapping approach enables potentially low-latency control.
Simplified Control Pipeline: By eliminating intermediate pose estimation, PoseLess simplifies the robotic hand control pipeline.

A novel framework (PoseLess) for direct mapping of monocular images to robot joint angles using a VLM: This bypasses explicit pose estimation and projects images for robust, morphology-agnostic feature extraction.
A synthetic data pipeline that generates infinite training examples: This is achieved by randomizing joint angles and domain-randomizing visual features, eliminating reliance on costly labeled datasets and ensuring robustness to real-world variations. The synthetic data is generated using a detailed 3D model of a “shadow-hand” with 25 degrees of freedom and physiologically plausible joint angle ranges. Controlled rendering parameters (fixed lighting, camera angle, white background) are used, while hand textures and materials are randomized.
Evidence of the model’s cross-morphology generalization: The model demonstrates the ability to mimic human hand movements despite being trained solely on robot hand data.
Evidence that depth-free control is possible: This paves the way for adoption with cameras not supporting depth estimation.
Validation of the poseless control paradigm: Experiments show competitive performance in joint angle prediction accuracy (reduced mean squared error) when trained solely on synthetic data.

Robotic Hand Control: Provides a robust and data-efficient approach for controlling robotic hands.
Prosthetics: The cross-morphology generalization capability opens avenues for developing more adaptable prosthetic hands.
Human-Robot Interaction: Enables more intuitive and flexible interaction by potentially allowing robots to understand and mimic human hand movements without explicit pose information.
Robotic Manipulation in Diverse Environments: The depth-free nature of PoseLess could be beneficial in scenarios where depth estimation is unreliable, such as with monocular vision setups.
Simplifying Hardware Requirements: Eliminating the dependency on depth information can broaden the accessibility and potential applications of robotic hand control by reducing hardware complexity.

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