Robot See Robot Do: Imitating Articulated Object Manipulation with Monocular 4D Reconstruction (2409.18121v1)

Abstract: Humans can learn to manipulate new objects by simply watching others; providing robots with the ability to learn from such demonstrations would enable a natural interface specifying new behaviors. This work develops Robot See Robot Do (RSRD), a method for imitating articulated object manipulation from a single monocular RGB human demonstration given a single static multi-view object scan. We first propose 4D Differentiable Part Models (4D-DPM), a method for recovering 3D part motion from a monocular video with differentiable rendering. This analysis-by-synthesis approach uses part-centric feature fields in an iterative optimization which enables the use of geometric regularizers to recover 3D motions from only a single video. Given this 4D reconstruction, the robot replicates object trajectories by planning bimanual arm motions that induce the demonstrated object part motion. By representing demonstrations as part-centric trajectories, RSRD focuses on replicating the demonstration's intended behavior while considering the robot's own morphological limits, rather than attempting to reproduce the hand's motion. We evaluate 4D-DPM's 3D tracking accuracy on ground truth annotated 3D part trajectories and RSRD's physical execution performance on 9 objects across 10 trials each on a bimanual YuMi robot. Each phase of RSRD achieves an average of 87% success rate, for a total end-to-end success rate of 60% across 90 trials. Notably, this is accomplished using only feature fields distilled from large pretrained vision models -- without any task-specific training, fine-tuning, dataset collection, or annotation. Project page: https://robot-see-robot-do.github.io

• The paper introduces Robot See Robot Do, a novel method that transfers human object manipulation to robots via monocular 4D reconstruction, achieving a 60% overall success rate.
• It employs a 4D Differentiable Part Model with ARAP regularization to extract and optimize 3D part motion from RGB videos, facilitating precise bimanual trajectory planning and grasping.
• The approach reduces reliance on task-specific training by enabling zero-shot imitation learning, demonstrating versatility across diverse articulated objects.

Robot See Robot Do: Imitating Articulated Object Manipulation with Monocular 4D Reconstruction

Abstract Overview

The paper, "Robot See Robot Do: Imitating Articulated Object Manipulation with Monocular 4D Reconstruction," introduces a method for transferring human object manipulation actions to robots. This method, Robot See Robot Do (RSRD), leverages the ability to perform object-centric manipulations from single monocular RGB videos captured during human demonstrations. The research establishes a novel 4D Differentiable Part Model (4D-DPM) which significantly aids in the recovery of 3D part motions from these videos. The paper's findings show notable physical execution performance using a bimanual YuMi robot, achieving a 60% end-to-end success rate across various objects without task-specific training or fine-tuning.

Methodological Insight

4D-Differentiable Part Models (4D-DPM)

The core of the proposed system revolves around 4D-DPM, which utilizes a part-centric feature field to iteratively optimize object part motion recovery through differentiable rendering. The methodology includes:

1. Static Multi-view Object Scanning: A 3D model of the object is created using Gaussian Splatting (3DGS) to represent the object's appearance and segment it into parts.
2. Feature Embedding: Using DINO feature fields, the paper embeds dense part descriptors within the model to track part movement in the video.
3. Optimization Paradigm: An analysis-by-synthesis approach allows the model to iteratively match 3D part motion to visual observations through differentiable rendering, aided by temporal smoothness and as-rigid-as-possible (ARAP) regularization priors.

Robot Execution Phase

For robot deployment, RSRD considers the following phases:

1. Pose Registration: The robot recognizes the object in its workspace using stereo depth, initializing the pose for subsequent motion recovery.
2. Planning Bimanual Trajectories: RSRD plans end-effector trajectories that emulate the human-demonstrated object motion, while considering the robot's morphological constraints.
3. Grasp Planning: The method includes selecting object parts based on detected hand-part interactions from the demonstration, planning feasible grasps, and ensuring the robot can manipulate the object parts through the full trajectory.

Evaluation and Experimental Results

RSRD was evaluated on a range of 9 articulated objects across 90 trials, achieving specific success rates for different phases of the robot execution pipeline:

• Pose Registration: 94%
• Trajectory Planning: 87%
• Initial Grasps: 83%
• Motion Execution: 85%

End-to-end, RSRD accomplished a 60% success rate, a robust outcome given the diversity of the objects involved and the constraints of using monocular video inputs without additional training data.

Motion Recovery Ablations

The ablation studies demonstrate the importance of key components in motion recovery:

• ARAP Regularization: Removal led to a significant increase in tracking error, highlighting its importance in maintaining the coherence of object motion.
• DINO Features: Utilization of DINO features for tracking vastly outperformed photometric methods, underscoring the robustness imparted by large pretrained vision models.

Implications and Future Directions

The findings suggest several practical and theoretical implications:

• Implications for Imitation Learning: By enabling zero-shot learning from monocular human interaction videos, RSRD reduces the need for extensive task-specific training data, paving the way for more dynamic and adaptable robot learning systems.
• Flexibility in Robotics: The object-centric approach allows demonstrations to be transferred across different robots and scenarios, increasing the method's versatility in practical applications.

Future Developments

Future research could enhance RSRD by:

• Adapting to Initial Configuration Variations: Further studies could improve robustness by allowing flexibility in initial object configurations.
• Automating Segmentation: Increasing automation in object and part segmentation could reduce manual interventions, making the system more scalable.
• Handling Complex Demonstrations: Enhancing the system's ability to handle demonstrations with complex backgrounds or partial occlusions would improve its applicability to more naturalistic settings.
• Non-Prehensile Manipulations: Extending the method to include non-prehensile strategies would broaden the scope of manipulable objects and actions.

Conclusion

The paper presents a compelling approach to robot learning from human demonstrations, leveraging advanced vision models and innovative part-motion recovery techniques. RSRD's ability to perform object part manipulations using monocular video inputs represents a significant step forward in the field of robot imitation learning, highlighting the potential for more adaptable and efficient robotic systems in dynamic environments.