The state recognition of the environment and objects by robots is generally based on the judgement of the current state as a classification problem. On the other hand, state changes of food in cooking happen continuously and need to be captured not only at a certain time point but also continuously over time. In addition, the state changes of food are complex and cannot be easily described by manual programming. Therefore, we propose a method to recognize the continuous state changes of food for cooking robots through the spoken language using pre-trained large-scale vision-language models. By using models that can compute the similarity between images and texts continuously over time, we can capture the state changes of food while cooking. We also show that by adjusting the weighting of each text prompt based on fitting the similarity changes to a sigmoid function and then performing black-box optimization, more accurate and robust continuous state recognition can be achieved. We demonstrate the effectiveness and limitations of this method by performing the recognition of water boiling, butter melting, egg cooking, and onion stir-frying.
Automated cooking machine is a goal for the future. The main aim is to make the cooking process easier, safer, and create human welfare. To allow robots to accurately perform the cooking activities, it is important for them to understand the cooking environment and recognize the objects, especially correctly identifying the state of the cooking objects. This will significantly improve the correctness of the following cooking recipes. In this project, several parts of the experiment were conducted to design a robust deep convolutional neural network for classifying the state of the cooking obje…
Using the Functional Object-Oriented Network, we have implemented three search algorithms for generating the task trees for the given goal nodes. The approach, process, and results are written in this paper.
People often watch videos on the web to learn how to cook new recipes, assemble furniture or repair a computer. We wish to enable robots with the very same capability. This is challenging; there is a large variation in manipulation actions and some videos even involve multiple persons, who collaborate by sharing and exchanging objects and tools. Furthermore, the learned representations need to be general enough to be transferable to robotic systems. On the other hand, previous work has shown that the space of human manipulation actions has a linguistic, hierarchical structure that relates acti…
University of South Florida（南佛罗里达大学）；Department of Computer Science and Engineering（计算机科学与工程系）；Robot Perception and Action Lab（机器人感知与行动实验室）
This paper describes our recent research effort to bring the computer intelligence into the physical world so that robots could perform physically interactive manipulation tasks. Our proposed approach first gives robots the ability to learn manipulation skills by "watching" online instructional videos. After "watching" over 200 instructional videos, a functional object-oriented network (FOON) is constructed to represent the observed manipulation skills. Using the network, robots can take a high-level task command such as "I want BBQ Ribs for dinner," decipher the task goal, seek the correct ob…
Search algorithms are applied where data retrieval with specified specifications is required. The motivation behind developing search algorithms in Functional Object-Oriented Networks is that most of the time, a certain recipe needs to be retrieved or ingredients for a certain recipe needs to be determined. According to the introduction, there is a time when execution of an entire recipe is not available for a robot thus prompting the need to retrieve a certain recipe or ingredients. With a quality FOON, robots can decipher a task goal, find the correct objects at the required states on which …
University of South Florida（南佛罗里达大学）；Brown University（布朗大学）
Flexible task planning continues to pose a difficult challenge for robots, where a robot is unable to creatively adapt their task plans to new or unseen problems, which is mainly due to the limited knowledge it has about its actions and world. Motivated by a human's ability to adapt, we explore how task plans from a knowledge graph, known as the Functional Object- Oriented Network (FOON), can be generated for novel problems requiring concepts that are not readily available to the robot in its knowledge base. Knowledge from 140 cooking recipes are structured in a FOON knowledge graph, which is …
The Chinese University of Hong Kong（香港中文大学）；Wuhan University（武汉大学）；Idiap Research Institute（Idiap研究所）
This letter describes an approach to achieve well-known Chinese cooking art stir-fry on a bimanual robot system. Stir-fry requires a sequence of highly dynamic coordinated movements, which is usually difficult to learn for a chef, let alone transfer to robots. In this letter, we define a canonical stir-fry movement, and then propose a decoupled framework for learning this deformable object manipulation from human demonstration. First, the dual arms of the robot are decoupled into different roles (a leader and follower) and learned with classical and neural network-based methods separately, the…
In robotics, knowing the object states and recognizing the desired states are very important. Objects at different states would require different grasping. To achieve different states, different manipulations would be required, as well as different grasping. To analyze the objects at different states, a dataset of cooking objects was created. Cooking consists of various cutting techniques needed for different dishes (e.g. diced, julienne etc.). Identifying each of this state of cooking objects by the human can be difficult sometimes too. In this paper, we have analyzed seven different cooking …
This paper introduces a taxonomy of manipulations as seen especially in cooking for 1) grouping manipulations from the robotics point of view, 2) consolidating aliases and removing ambiguity for motion types, and 3) provide a path to transferring learned manipulations to new unlearned manipulations. Using instructional videos as a reference, we selected a list of common manipulation motions seen in cooking activities grouped into similar motions based on several trajectory and contact attributes. Manipulation codes are then developed based on the taxonomy attributes to represent the manipulati…
Learning Multimodal Attention for Manipulating Deformable Objects with Changing States
Future Robotics Organization, Waseda University（早稻田大学未来机器人组织）；Microsoft Research Asia（微软亚洲研究院）；Department of Modern Mechanical Engineering, Waseda University（早稻田大学现代机械工程系）；Artificial Intelligence Laboratories, Fujitsu Limited（Fujitsu 人工智能实验室）；Center for Technology Innovation - Controls and Robotics, Research & Development Group, Hitachi, Ltd.（富士通技术研发集团技术创新中心 - 控制与机器人）；Faculty of Science and Engineering, Waseda University（早稻田大学工学部）；National Institute of Advanced Science and Technology（国家先进科学研究院）
The Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）；Machine Learning Department, Carnegie Mellon University（卡内基梅隆大学机器学习系）
Cutting is a common form of manipulation when working with divisible objects such as food, rope, or clay. Cooking in particular relies heavily on cutting to divide food items into desired shapes. However, cutting food is a challenging task due to the wide range of material properties exhibited by food items. Due to this variability, the same cutting motions cannot be used for all food items. Sensations from contact events, e.g., when placing the knife on the food item, will also vary depending on the material properties, and the robot will need to adapt accordingly. In this paper, we propose u…
Skolkovo Institute of Science and Technology (Skoltech)（斯科尔科沃科学技术研究院（斯科尔特克））
We present ZoomTouch - a breakthrough technology for multi-user control of robot from Zoom in real-time by DNN-based gesture recognition. The users from digital world can have a video conferencing and manipulate the robot to make the dexterous manipulations with tangible objects. As the scenario, we proposed the remote COVID-19 test Laboratory to considerably reduce the time to receive the data and substitute medical assistant working in protective gear in close proximity with infected cells. The proposed technology suggests a new type of reality, where multi-users can jointly interact with re…
In this paper, a comparison of reinforcement learning algorithms and their performance on a robot box pushing task is provided. The robot box pushing problem is structured as both a single-agent problem and also a multi-agent problem. A Q-learning algorithm is applied to the single-agent box pushing problem, and three different Q-learning algorithms are applied to the multi-agent box pushing problem. Both sets of algorithms are applied on a dynamic environment that is comprised of static objects, a static goal location, a dynamic box location, and dynamic agent positions. A simulation environm…
Queensland University of Technology（昆士兰科技大学）；Australian Centre for Robotic Vision（澳大利亚机器人视觉中心）
We introduce a new challenge for computer and robotic vision, the first ACRV Robotic Vision Challenge, Probabilistic Object Detection. Probabilistic object detection is a new variation on traditional object detection tasks, requiring estimates of spatial and semantic uncertainty. We extend the traditional bounding box format of object detection to express spatial uncertainty using gaussian distributions for the box corners. The challenge introduces a new test dataset of video sequences, which are designed to more closely resemble the kind of data available to a robotic system. We evaluate prob…
This paper presents an approach for autonomous underwater robots to visually detect and identify divers. The proposed approach enables an autonomous underwater robot to detect multiple divers in a visual scene and distinguish between them. Such methods are useful for robots to identify a human leader, for example, in multi-human/robot teams where only designated individuals are allowed to command or lean a team of robots. Initial diver identification is performed using the Faster R-CNN algorithm with a region proposal network which produces bounding boxes around the divers' locations. Subseque…
University of the West of England（西英格兰大学）；University of Oxford（牛津大学）
This paper introduces a draft open standard for the robot equivalent of an aircraft flight data recorder, which we call an ethical black box. This is a device, or software module, capable of securely recording operational data (sensor, actuator and control decisions) for a social robot, in order to support the investigation of accidents or near-miss incidents. The open standard, presented as an annex to this paper, is offered as a first draft for discussion within the robot ethics community. Our intention is to publish further drafts following feedback, in the hope that the standard will becom…
Queensland University of Technology（昆士兰科技大学）；DATA61, CSIRO（联邦科学与工业研究组织DATA61）
In object detection, false negatives arise when a detector fails to detect a target object. To understand why object detectors produce false negatives, we identify five 'false negative mechanisms', where each mechanism describes how a specific component inside the detector architecture failed. Focusing on two-stage and one-stage anchor-box object detector architectures, we introduce a framework for quantifying these false negative mechanisms. Using this framework, we investigate why Faster R-CNN and RetinaNet fail to detect objects in benchmark vision datasets and robotics datasets. We show th…
Interactively Robot Action Planning with Uncertainty Analysis and Active Questioning by Large Language Model
Waseda University（早稻田大学）；Fujitsu Limited（富士通有限公司）；Waseda Research Institute for Science and Engineering（早稻田大学科学与工程研究院）；National Institute of Advanced Industrial Science and Technology（日本产业技术综合研究所）
Simulation-Aided Policy Tuning for Black-Box Robot Learning
School of Information and Electrical Engineering, Hangzhou City University（杭州城市大学信息与电气工程学院）；RWTH Aachen University（亚琛工业大学）；Aalto University（阿尔托大学）；Uppsala University（乌普萨拉大学）；College of Electrical Engineering, Zhejiang University（浙江大学电气工程学院）；Huzhou Institute of Zhejiang University（浙江大学湖州研究院）
Johns Hopkins University（约翰斯·霍普金斯大学）；National University of Singapore（新加坡国立大学）
A robot cannot lift up an object if it is not feasible to do so. However, in most research on robot lifting, "feasibility" is usually presumed to exist a priori. This paper proposes a three-step method for a humanoid robot to reason about the feasibility of lifting a heavy box with physical properties that are unknown to the robot. Since feasibility of lifting is directly related to the physical properties of the box, we first discretize a range for the unknown values of parameters describing these properties and tabulate all valid optimal quasi-static lifting trajectories generated by simulat…
Grasp is an essential skill for robots to interact with humans and the environment. In this paper, we build a vision-based, robust and real-time robotic grasp approach with fully convolutional neural network. The main component of our approach is a grasp detection network with oriented anchor boxes as detection priors. Because the orientation of detected grasps is significant, which determines the rotation angle configuration of the gripper, we propose the Orientation Anchor Box Mechanism to regress grasp angle based on predefined assumption instead of classification or regression without any …
Learning Coordinated Visuomotor Box-Pushing from Solo Demonstrations
National Institute of Advanced Industrial Science and Technology (AIST)（国立产业技术综合研究所（AIST））；Institute of Science Tokyo（东京科学大学）；The University of Tokyo（东京大学）
Inria（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；Université de Lorraine（洛林大学）
The most data-efficient algorithms for reinforcement learning in robotics are model-based policy search algorithms, which alternate between learning a dynamical model of the robot and optimizing a policy to maximize the expected return given the model and its uncertainties. Among the few proposed approaches, the recently introduced Black-DROPS algorithm exploits a black-box optimization algorithm to achieve both high data-efficiency and good computation times when several cores are used; nevertheless, like all model-based policy search approaches, Black-DROPS does not scale to high dimensional…
University of South Florida（南佛罗里达大学）；Technical University of Munich（慕尼黑工业大学）；University of Illinois - Urbana Champaign（伊利诺伊大学厄巴纳-香槟分校）
In reality, there is still much to be done for robots to be able to perform manipulation actions with full autonomy. Complicated manipulation tasks, such as cooking, may still require a person to perform some actions that are very risky for a robot to perform. On the other hand, some other actions may be very risky for a human with physical disabilities to perform. Therefore, it is necessary to balance the workload of a robot and a human based on their limitations while minimizing the effort needed from a human in a collaborative robot (cobot) set-up. This paper proposes a new version of our f…
Ain Shams University（艾因沙姆斯大学）；Jacobs University（雅各布大学）；Stanford University（斯坦福大学）；KTH Royal Institute of Technology（KTH皇家理工学院）
Reinforcement learning (RL) is capable of sophisticated motion planning and control for robots in uncertain environments. However, state-of-the-art deep RL approaches typically lack safety guarantees, especially when the robot and environment models are unknown. To justify widespread deployment, robots must respect safety constraints without sacrificing performance. Thus, we propose a Black-box Reachability-based Safety Layer (BRSL) with three main components: (1) data-driven reachability analysis for a black-box robot model, (2) a trajectory rollout planner that predicts future actions and ob…
LIRIS, CNRS UMR 5205（里昂图像与信息系统实验室，法国国家科学研究中心 UMR 5205）；Ecole Centrale de Lyon（里昂中央理工学院）
We present a developmental framework based on a long-term memory and reasoning mechanisms (Vision Similarity and Bayesian Optimisation). This architecture allows a robot to optimize autonomously hyper-parameters that need to be tuned from any action and/or vision module, treated as a black-box. The learning can take advantage of past experiences (stored in the episodic and procedural memories) in order to warm-start the exploration using a set of hyper-parameters previously optimized from objects similar to the new unknown one (stored in a semantic memory). As example, the system has been used…
