-
公开(公告)号：EP4410497A1
公开(公告)日：2024-08-07
申请号：EP23154371.1
申请日：2023-02-01
IPC分类号： B25J9/16
CPC分类号： B25J9/1697 , G05B2219/3755520130101 , G05B2219/4005320130101 , G05B2219/4056420130101 , G05B2219/4506320130101 , B25J9/1679
摘要： Bin packing refers to a robot grasping objects that can define random or arbitrary poses, and placing them into a container or bin. It is recognized herein, however, that current approaches to robotic packing lack efficiency and capabilities. In particular, current approaches often rely on known objects having regular shapes and/or do not properly or efficiently make use of the packing space, due to various technical challenges in doing so. An autonomous system can include a robot that packs containers with objects that define non-rigid or irregular shapes, such that the packed objects are stable after placement and are not adjusted (e.g., pushed or pulled) by the robot after placement in the container.
-
公开(公告)号：EP4367595A1
公开(公告)日：2024-05-15
申请号：EP22761332.0
申请日：2022-08-10
发明人： XIA, Wei Xi , SOLOWJOW, Eugen , TAMASKAR, Shashank , APARICIO OJEA, Juan L. , CLAUSSEN, Heiko , UGALDE DIAZ, Ines , SHAHAPURKAR, Yash , SATHYA NARAYANAN, Gokul Narayanan , WEN, Chengtao
CPC分类号： G06N3/047 , B25J9/163 , G06V10/82 , G06T5/50 , G06T2207/2008120130101 , G06T2207/2008420130101 , G06T5/60 , G06N3/0464 , G06N3/094 , G06N3/0475
-
公开(公告)号：EP4332900A1
公开(公告)日：2024-03-06
申请号：EP22192858.3
申请日：2022-08-30
发明人： MOURA CIRILO ROCHA, Eduardo , ERDOGAN, Husnu Melih , SOLOWJOW, Eugen , UGALDE DIAZ, Ines , SHAHAPURKAR, Yash , TIAN, Nan , BATSII, Pavlo , SCHÜTTE, Christopher
IPC分类号： G06T7/73
摘要： It is recognized herein that current approaches to robotic picking lack efficiency and capabilities. In particular, current approaches often do not properly or efficiently estimate the pose of bins, due to various technical challenges in doing so, which can impact grasp computations and overall performance of a given robot. The pose of the bin can be determined or estimated based on depth images. Such bin pose estimation can be performed during runtime of a given robot, such that grasping can be enhanced due to the bin pose estimations.
-
公开(公告)号：EP4326496A1
公开(公告)日：2024-02-28
申请号：EP21734591.7
申请日：2021-05-25
发明人： APARICIO OJEA, Juan L. , CLAUSSEN, Heiko , UGALDE DIAZ, Ines , SATHYA NARAYANAN, Gokul Narayanan , SOLOWJOW, Eugen , WEN, Chengtao , XIA, Wei Xi , SHAHAPURKAR, Yash , TAMASKAR, Shashank
IPC分类号： B25J9/16
-
公开(公告)号：EP4326495A1
公开(公告)日：2024-02-28
申请号：EP21733298.0
申请日：2021-05-25
发明人： APARICIO OJEA, Juan L. , CLAUSSEN, Heiko , UGALDE DIAZ, Ines , SATHYA NARAYANAN, Gokul Narayanan , SOLOWJOW, Eugen , WEN, Chengtao , XIA, Wei Xi , SHAHAPURKAR, Yash , TAMASKAR, Shashank
IPC分类号： B25J9/16
-
公开(公告)号：EP4292772A1
公开(公告)日：2023-12-20
申请号：EP22179085.0
申请日：2022-06-15
IPC分类号： B25J9/16
摘要： In some cases, images and depth maps can define bins with objects in random configurations. It is recognized herein that current approaches to training deep neural networks to perform grasp computations lack capabilities and efficiencies, such that the resulting grasp computations and grasps can be imprecise or cumbersome, among other shortcomings. Synthetic depth images can be labeled with grasp annotations that are generated based on heuristic-based analyses, so as to define annotated synthetic datasets. The annotated synthetic datasets can be used to train neural networks to determine the best grasp locations for different objects arranged in a variety of positions with respect to each other.
-
7.发明公开
公开(公告)号：EP4102423A1
公开(公告)日：2022-12-14
申请号：EP21178050.7
申请日：2021-06-07
发明人： WEN, Chengtao , APARICIO OJEA, Juan L. , UGALDE DIAZ, Ines , SATHYA NARAYANAN, Gokul Narayanan , SOLOWJOW, Eugen , XIA, Wei Xi , SHAHAPURKAR, Yash , TAMASKAR, Shashank , CLAUSSEN, Heiko
IPC分类号： G06Q10/06
摘要： A method for automatically generating a bill of process in a manufacturing system comprising: receiving design information representative of a product to be produced; iteratively performing simulations of the manufacturing system; identifying manufacturing actions based on the simulations; optimizing the identified manufacturing actions to efficiently produce the product to be produced; generating, by the manufacturing system, a bill of process for producing the product. Simulations may be performed using a digital twin of the product being produced and a digital twin of the environment. System actions are optimized using a reinforcement learning technique to automatically produce a bill of process based on the design information of the product and task specifications.
-
8.发明公开
公开(公告)号：EP4060439A1
公开(公告)日：2022-09-21
申请号：EP21163684.0
申请日：2021-03-19
发明人： APARICIO OJEA, Juan L. , CLAUSSEN, Heiko , UGALDE DIAZ, Ines , SHAHAPURKAR, Yash , SOLOWJOW, Eugen , WEN, Chengtao , XIA, Wei Xi , SATHYA NARAYANAN, Gokul Narayanan , TAMASKAR, Shashank
IPC分类号： G05B19/418 , B25J9/16
摘要： A computer-implemented method for designing execution of a process by a robotic cell includes obtaining a process goal and one or more process constraints. The method includes accessing a library of constructs and a library of skills. Each construct includes a digital representation of a component of the robotic cell or a geometric transformation of the robotic cell. Each skill includes a functional description for using a robot of the robotic cell to interact with a physical environment to perform a skill objective. The method uses a simulation engine to simulate a multiplicity of designs, wherein each design is characterized by a combination of constructs and skills to achieve the process goal, and determine a set of feasible designs that meet the one or more process constraints. The method includes outputting recommended designs from the set of feasible designs.
-
公开(公告)号：EP4401049A1
公开(公告)日：2024-07-17
申请号：EP23151339.1
申请日：2023-01-12
CPC分类号： B25J9/1697 , G05B2219/3904520130101 , G05B2219/3912520130101 , G05B2219/3953620130101 , G05B2219/3954220130101 , G05B2219/3954320130101 , G05B2219/4015520130101 , G05B2219/4062920130101 , B25J9/1612 , G06V20/64 , G06V10/48 , G06V2201/0620220101 , G06V10/431 , G06V20/10
摘要： An autonomous system can detect out-of-distribution (OOD) data in robotic grasping systems, based on evaluating image inputs of the robotic grasping systems. Furthermore, the system makes various decisions based on detecting the OOD data, so as to avoid inefficient or hazardous situations or other negative consequences (e.g., damage to products). For example, the system can determine whether a suction-based gripper is optimal for grasping objects in a given scene, based at least in part on determining whether an image defines OOD data.
-
公开(公告)号：EP4327299A1
公开(公告)日：2024-02-28
申请号：EP21733302.0
申请日：2021-05-25
-
-
-
-
-
-
-
-
-
搜索结果
13 条记录 (耗时 0.011 秒)
