Nicolás Rojas

Xiaoshuai Chen, Wei Chen, Dongmyoung Lee et al.

End-to-end robot learning, particularly for long-horizon tasks, often results in unpredictable outcomes and poor generalization. To address these challenges, we propose a novel Therblig-Based Backbone Framework (TBBF) as a fundamental structure to enhance interpretability, data efficiency, and generalization in robotic systems. TBBF utilizes expert demonstrations to enable therblig-level task decomposition, facilitate efficient action-object mapping, and generate adaptive trajectories for new scenarios. The approach consists of two stages: offline training and online testing. During the offline training stage, we developed the Meta-RGate SynerFusion (MGSF) network for accurate therblig segmentation across various tasks. In the online testing stage, after a one-shot demonstration of a new task is collected, our MGSF network extracts high-level knowledge, which is then encoded into the image using Action Registration (ActionREG). Additionally, Large Language Model (LLM)-Alignment Policy for Visual Correction (LAP-VC) is employed to ensure precise action registration, facilitating trajectory transfer in novel robot scenarios. Experimental results validate these methods, achieving 94.37% recall in therblig segmentation and success rates of 94.4% and 80% in real-world online robot testing for simple and complex scenarios, respectively. Supplementary material is available at: https://sites.google.com/view/therbligsbasedbackbone/home

Angus B. Clark, Visakan Mathivannan, Nicolas Rojas

Many have explored the application of continuum robot manipulators for minimally invasive surgery, and have successfully demonstrated the advantages their flexible design provides -- with some solutions having reached commercialisation and clinical practice. However, the usual high complexity and closed-nature of such designs has traditionally restricted the shared development of continuum robots across the research area, thus impacting further progress and the solution of open challenges. In order to close this gap, this paper introduces ENDO, an open-source 3-segment continuum robot manipulator with control and actuation mechanism, whose focus is on simplicity, affordability, and accessibility. This robotic system is fabricated from low cost off-the-shelf components and rapid prototyping methods, and its information for implementation (and that of future iterations), including CAD files and source code, is available to the public on the Open Source Medical Robots initiative's repository on GitHub (https://github.com/OpenSourceMedicalRobots), with the control library also available directly from Arduino. Herein, we present details of the robot design and control, validate functionality by experimentally evaluating its workspace, and discuss possible paths for future development.

Dongmyoung Lee, Wei Chen, Nicolas Rojas

Object recognition and object pose estimation in robotic grasping continue to be significant challenges, since building a labelled dataset can be time consuming and financially costly in terms of data collection and annotation. In this work, we propose a synthetic data generation method that minimizes human intervention and makes downstream image segmentation algorithms more robust by combining a generated synthetic dataset with a smaller real-world dataset (hybrid dataset). Annotation experiments show that the proposed synthetic scene generation can diminish labelling time dramatically. RGB image segmentation is trained with hybrid dataset and combined with depth information to produce pixel-to-point correspondence of individual segmented objects. The object to grasp is then determined by the confidence score of the segmentation algorithm. Pick-and-place experiments demonstrate that segmentation trained on our hybrid dataset (98.9%, 70%) outperforms the real dataset and a publicly available dataset by (6.7%, 18.8%) and (2.8%, 10%) in terms of labelling and grasping success rate, respectively. Supplementary material is available at https://sites.google.com/view/synthetic-dataset-generation.