Adapting Skills to Novel Grasps: A Self-Supervised Approach

Abstract: In this paper, we study the problem of adapting manipulation trajectories involving grasped objects (e.g. tools) defined for a single grasp pose to novel grasp poses. A common approach to address this is to define a new trajectory for each possible grasp explicitly, but this is highly inefficient. Instead, we propose a method to adapt such trajectories directly while only requiring a period of self-supervised data collection, during which a camera observes the robot's end-effector moving with the object rigidly grasped. Importantly, our method requires no prior knowledge of the grasped object (such as a 3D CAD model), it can work with RGB images, depth images, or both, and it requires no camera calibration. Through a series of real-world experiments involving 1360 evaluations, we find that self-supervised RGB data consistently outperforms alternatives that rely on depth images including several state-of-the-art pose estimation methods. Compared to the best-performing baseline, our method results in an average of 28.5% higher success rate when adapting manipulation trajectories to novel grasps on several everyday tasks. Videos of the experiments are available on our webpage at https://www.robot-learning.uk/adapting-skills

• The paper introduces a self-supervised approach that adapts learned manipulation skills to new grasp configurations without human intervention.
• It leverages a vision-based alignment network trained on RGB data, eliminating the need for prior object knowledge or precise calibrations.
• Experimental results demonstrate a 28.5% improvement in success rates over conventional depth-based methods in unstructured environments.

Adaptation of Robotic Manipulation Skills to Novel Grasp Poses

The paper, "Adapting Skills to Novel Grasps: A Self-Supervised Approach," addresses the efficient adaptation of robotic manipulation trajectories to novel grasp poses without the conventional need for explicit trajectory definition. The authors propose a self-supervised method that allows a robot to autonomously adjust manipulation skills learned for a single grasp pose to any new grasp configuration. This capability is crucial for practical robotic deployments where various grasp poses can occur due to environmental or interaction variations.

Core Contributions

The central contribution of this research is developing a method that bypasses the usual requirements of prior object knowledge or camera calibration. The proposed method incorporates:

1. Vision-Based Alignment Network: The authors introduce an alignment network that predicts the corrective transformation required to adapt a skill trajectory to align with novel grasp poses. This network is trained via data collected in a self-supervised manner, requiring only a few minutes of data gathering from robot motions in front of a camera.
2. Self-Supervised Data Collection: Involving no human intervention, this process allows the robot to autonomously emulate various grasps by manipulating the object with a single known grasp pose. Images captured during this phase form the dataset for training the alignment network.
3. No Prior Object Knowledge: The method operates without 3D CAD models or object-specific data, making it applicable in unstructured and unknown environments. This aspect highlights the robustness and applicability of the method across different object types.
4. RGB Image Utilization: The study demonstrated that self-supervised learning using solely RGB images can outperform depth-based methods, even those relying on advanced pose estimation techniques.

Numerical Results and Analysis

The experiments conducted validated the enhanced adaptability and accuracy of the proposed approach over state-of-the-art methods. The investigation involved real-world tasks such as peg-in-hole insertions and manipulation requiring precision like hammering or spooning, highlighting a 28.5% higher success rate compared to existing techniques. The method demonstrated robust performance even in challenging scenarios with poorly textured or transparent objects, in which conventional depth-based methods generally struggle.

Implications and Future Directions

The implications of this research are significant in terms of both practical applications and theoretical advancements:

• Practical Impact: The method provides a practical solution for robots in dynamic and unstructured environments, where grasp poses can vary, and explicit pre-programming of every possible scenario is infeasible.
• Theory and Modelling: This approach contributes to the broader conversation about self-supervised learning in robotics. It underlines the potential of leveraging RGB data, challenging conventional reliance on depth sensing for pose estimation and adaptation.

For future work, extending this method’s capability to encompass dynamic object environments and enhancing its generalization to different object categories without retraining could be explored. Additionally, integrating this method with advanced robotic action models could further broaden its applicability in more complex, real-world tasks.

In conclusion, the presented self-supervised strategy for adapting skills to new grasp poses marks an advancement in making robotic systems more versatile and autonomous in executing precise manipulation tasks across diverse scenarios. The reliance on vision-based self-supervision highlights a step towards more resource-efficient and adaptable robotic systems.