Incremental Learning for Robot Perception through HRI

Scene understanding and object recognition is a difficult to achieve yet crucial skill for robots. Recently, Convolutional Neural Networks (CNN), have shown success in this task. However, there is still a gap between their performance on image datasets and real-world robotics scenarios. We present a novel paradigm for incrementally improving a robot's visual perception through active human interaction. In this paradigm, the user introduces novel objects to the robot by means of pointing and voice commands. Given this information, the robot visually explores the object and adds images from it to re-train the perception module. Our base perception module is based on recent development in object detection and recognition using deep learning. Our method leverages state of the art CNNs from off-line batch learning, human guidance, robot exploration and incremental on-line learning

Bridging Low-level Geometry to High-level Concepts in Visual Servoing of Robot Manipulation Task Using Event Knowledge Graphs and Vision-Language Models

In this paper, we propose a framework of building knowledgeable robot control in the scope of smart human-robot interaction, by empowering a basic uncalibrated visual servoing controller with contextual knowledge through the joint usage of event knowledge graphs (EKGs) and large-scale pretrained vision-language models (VLMs). The framework is expanded in twofold: first, we interpret low-level image geometry as high-level concepts, allowing us to prompt VLMs and to select geometric features of points and lines for motor control skills; then, we create an event knowledge graph (EKG) to conceptualize a robot manipulation task of interest, where the main body of the EKG is characterized by an executable behavior tree, and the leaves by semantic concepts relevant to the manipulation context. We demonstrate, in an uncalibrated environment with real robot trials, that our method lowers the reliance of human annotation during task interfacing, allows the robot to perform activities of daily living more easily by treating low-level geometric-based motor control skills as high-level concepts, and is beneficial in building cognitive thinking for smart robot applications

Robot eye-hand coordination learning by watching human demonstrations: a task function approximation approach

We present a robot eye-hand coordination learning method that can directly learn visual task specification by watching human demonstrations. Task specification is represented as a task function, which is learned using inverse reinforcement learning(IRL) by inferring differential rewards between state changes. The learned task function is then used as continuous feedbacks in an uncalibrated visual servoing(UVS) controller designed for the execution phase. Our proposed method can directly learn from raw videos, which removes the need for hand-engineered task specification. It can also provide task interpretability by directly approximating the task function. Besides, benefiting from the use of a traditional UVS controller, our training process is efficient and the learned policy is independent from a particular robot platform. Various experiments were designed to show that, for a certain DOF task, our method can adapt to task/environment variances in target positions, backgrounds, illuminations, and occlusions without prior retraining.Comment: Accepted in ICRA 201