This invention relates to kitchen appliances and more particularly to robotic kitchen apparatuses for operation with a fryer in a restaurant environment.
There are a number of challenges associated with operation of a fryer in a restaurant environment and additional challenges associated with automating the frying process.
First, placing a fryer basket in and out of the fryer to cook the food is fraught with danger arising from the hot oil. The fryer, the basket, the food items, and the oil have very high temperatures which can burn human workers during operation.
Employing automated processes to manipulate fryer baskets in a restaurant kitchen is not straightforward due to the wide range of operations that are involved with frying food items not the least of which is accurately locating and picking up the fryer basket. The so-called tolerance stack-up in today's kitchen environments is difficult to control because the shapes and tolerances of kitchen implements (baskets get bent) and kitchen equipment are not manufactured to high tolerances, and vision and depth sensors are not perfectly accurate.
Additionally, even if the fryer basket is successfully grasped and placed in the fryer, the food tends to clump while cooking in a fryer. Clumping introduces non-uniform temperatures to the food being cooked, or worse, leads to un-cooked food. Traditionally, a human operator will shake the fryer basket by hand, and observe the absence of clumping. The shaking process requires extra time and attention by a human operator which is undesirable. Automating this process is also challenging because of the large amounts of power, high frequency motion, and high peak forces associated with the shaking process, which are difficult to replicate in a reliable manner with traditional automation solutions. Another challenge is limiting or isolating the ‘shaking’ to the food item or utensil to be shook, and to avoid unduly shaking the balance of the automated food preparation equipment.
Additionally, while frying baskets of food, food debris separate from the food and remain in the oil. Over time, they burn and impart a burnt taste to other food cooked in the fryer. It is desirable to skim the fryer regularly to prevent particulates from burning. Traditionally, a human operator will skim the fryer between sets of baskets and remove the particulates from the skimmer by, e.g., banging the handle of the skimmer against the rim of a trash can.
Accordingly, a robotic kitchen assistant that overcomes the above mentioned challenges is still desirable.
A robotic kitchen assistant for frying includes a robotic arm, a fryer basket, and a robotic arm adapter adapted to releasably engage the fryer basket.
In embodiments, the robotic arm adapter includes opposing movable gripping members having a first open configuration when the gripping members are separated, and a second closed configuration when the gripping members are urged towards one another.
In embodiments, when the gripping members are in the second closed configuration, the opposing gripping members define a capture region to engage the fryer basket.
In embodiments, a utensil adapter assembly is secured to the fryer basket, and the capture region is sized to engage a target feature of the utensil adapter assembly.
In embodiments, the target feature of the utensil adapter assembly comprises a 3D shape such as a diamond, sphere, hourglass, or bulb.
In embodiments, each of the opposing gripping members comprises protruding fingers or teeth which register with a recess, cut-out, slot, or narrow region associated with the target feature of the utensil adapter assembly. In embodiments, angled faces or bearing surfaces guide the gripping teeth into the recess or clamping plane defined by the target feature.
In embodiments, a robotic kitchen assistant is operable to shake the fryer basket. The robotic kitchen assistant can include an agitator actuator operable to shake or move the gripping members while holding the fryer basket. In embodiments, the robotic kitchen assistant shakes or vibrates the fryer basket to de-clump food items therein, and without substantially moving links and components proximal to the agitator actuator. The agitator actuator and its motion is isolated from the balance of the robotic arm.
In embodiments, the robotic kitchen assistant includes a latching assembly to detachably mount the robotic stand to the ground in the kitchen restaurant. In embodiments, the latching assembly includes a weighted base enclosure, a bolt plate secured to the base and comprising a plurality of holes for bolts to be inserted therethrough, and a plurality of rollers to facilitate moving the robotic kitchen assistant when unbolted from the ground.
In embodiments, the robotic kitchen assistant is operable to pick up a skimmer and to manipulate the skimmer through the fryer to collect food debris from the fryer. The robotic kitchen assistant is further operable to dump the food debris from the skimmer by contacting a waste receptacle. In embodiments, an enclosed waste receptacle includes an air stream directed at the skimmer when the skimmer is inserted therein. The air stream separates the food from the skimmer and into the waste receptacle. In embodiments, sensors are used to direct the robotic kitchen assistant's motion as it skims the oil to maximize its effectiveness and efficiency.
In embodiments, a robotic kitchen system includes a robotic kitchen assistant, a fryer, a fryer basket transfer station, a waste receptacle including an air stream, and a plurality of utensils. In embodiments, the utensil is equipped with a utensil adapter assembly for the robotic kitchen assistant to engage during operation.
In embodiments, a universal robotic utensil pickup system includes a robotic arm adapter for connecting to a robotic arm and a utensil adapter assembly for connecting to the utensil, and preferably, the utensil handle. The robotic arm adapter assembly includes opposing movable gripping members having a first open configuration when the gripping members are separated, and a second closed configuration when the gripping members are urged towards one another. When the gripping members are in the second closed configuration, the opposing gripping members define an open capture region to engage a target feature of the utensil adapter assembly secured to the handle of the utensil.
Methods of frying include use of the robotic kitchen assistant and components for picking up the utensils, using the utensils, and removing debris from the utensils.
The description, objects and advantages of embodiments of the present invention will become apparent from the detailed description to follow, together with the accompanying drawings.
Before the present invention is described in detail, it is to be understood that this invention is not limited to particular variations set forth herein as various changes or modifications may be made to the invention described and equivalents may be substituted without departing from the spirit and scope of the invention. As will be apparent to those of skill in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has discrete components and features which may be readily separated from or combined with the features of any of the other several embodiments without departing from the scope or spirit of the present invention. In addition, many modifications may be made to adapt a particular situation, material, composition of matter, process, process act(s) or step(s) to the objective(s), spirit or scope of the present invention.
Methods recited herein may be carried out in any order of the recited events which is logically possible, as well as the recited order of events. Furthermore, where a range of values is provided, it is understood that every intervening value, between the upper and lower limit of that range and any other stated or intervening value in that stated range is encompassed within the invention. Also, it is contemplated that any optional feature of the inventive variations described may be set forth and claimed independently, or in combination with any one or more of the features described herein.
All existing subject matter mentioned herein (e.g., publications, patents, patent applications and hardware) is incorporated by reference herein in its entirety except insofar as the subject matter may conflict with that of the present invention (in which case what is present herein shall prevail).
Described herein is a robotic kitchen assistant for frying various food items in a fryer, and in embodiments, for removing food debris from the fryer.
Fryer Operation Overview
A top view and a rear perspective view of a kitchen environment 10 including a robotic kitchen assistant 20 for frying are shown in
The robotic kitchen assistant 20 can comprise a base or housing 22, robotic arm 24, and end effectors (not shown) as described, e.g., in international application no. PCT/US18/21066, filed Mar. 6, 2018, entitled “ROBOTIC KITCHEN ASSISTANT FOR PREPARING FOOD ITEMS IN A COMMERCIAL KITCHEN AND RELATED METHODS”, and international application no. PCT/US18/20948, filed Mar. 5, 2018, entitled “AUGMENTED REALITY-ENHANCED FOOD PREPARATION SYSTEM AND RELATED METHODS”, each of which is incorporated by reference in its entity for all purposes.
In embodiments, the robotic kitchen assistant includes a programmable processor, memory, cameras and sensors, displays, links, joints, actuators, power supply, and various user interface devices, to communicate, compute, and control movements of the robotic arm and end effectors including the gripping means described herein to operate with a fryer in a restaurant kitchen. In embodiments, and as described in the patent publications mentioned herein, the robotic kitchen assistant employs a trained neural network to locate and recognize food items and the utensils to manipulate.
With reference again to
Perspective views of the robotic kitchen assistant 20 for frying are shown in
Although the
The robotic kitchen assistant is operable to perform a wide range of steps including but not limited to actions otherwise taken by a human worker as the kitchen assistant fries various food items. In some embodiments, the robotic kitchen assistant is operable to perform a portion of the steps to fry, assisting the chef.
In a particular embodiment, a method comprises the following steps:
1. Chef prepares food and puts food in basket.
2. Chef puts basket in human/robot collaborative workspace (e.g., a table, rack, or custom basket transfer station). Optionally, a safety scanner is incorporated into the robotic kitchen assistant workspace to prevent robot and human from working in the same workspace at same time.
3. Robotic kitchen assistant identifies there is a basket, then inspects and classifies the food, assigning a cooking process to the food. Features for determining cooking process are: Food type, food initial thermodynamic state, food size, and food shape.
4. Robotic kitchen assistant monitors the current state of system (current # of baskets and their cook time) and optimizes the cooking process using machine learning optimization algorithms such as Monte Carlo Tree Search for quality or throughput or any given metric and schedules the appropriate cooking actions to hit that target.
5. Robotic kitchen assistant acts on the basket (e.g., dips in fryer, agitates, hangs to drip, removes from fryer).
The robotic kitchen assistant localizes the basket to be picked up and manipulated. The robotic kitchen assistant is operable to locate and manipulate a wide variety of kitchen implements in all 6 DOF in order to act on them. Nonlimiting techniques for localizing are described in patents and publications mentioned herein.
In embodiments, and as discussed further herein, basket pickup by the robotic kitchen assistant is enhanced by a robotic arm adapter assembly having a gripping feature, and a utensil adapter assembly comprising a target for the gripping feature to capture. The gripper apparatus or grasper enhancement mitigates error arising from noise in the location estimation and provides a more robust system to pick up or collect food preparation items such as a fryer basket.
6. The robotic kitchen assistant will place the basket in another human/robot collaborative workspace (e.g., a table or shelf such as the station 40, 50).
7. Chef can remove basket and perform temperature check. In embodiments, the robotic kitchen assistant removes the basket and a robotic arm or mechanism is used to insert a temperature probe to perform the temperature check. In embodiments, an IR camera is used to estimate temperatures.
8. In embodiments, the temperature data is fed back to optimize cooking process. Also, in embodiments, the robotic kitchen assistant employs control algorithms, such as model predictive control, on the fryer to preemptively turn on fryer when food is about to get dropped.
9. Optionally, food is manually cooked longer if more time is needed to cook the food, or a user input of additional cook time is fed back into system if more cook time is needed.
Basket Transfer Station
As described above in a frying method, the robotic kitchen assistant places the basket in a collaborative workspace such as a table, rack, or transfer station. With reference to
Gripper Operation
With reference to
Embodiments of the invention described herein overcome the challenge of the so-called tolerance stack-up in today's kitchen environments because it is difficult to control the shapes and tolerances of kitchen implements (baskets get bent), kitchen equipment is not manufactured to high tolerances, and vision and depth sensors are not perfectly accurate.
With reference to
The target feature 222 shown in
With reference to
The parallel actuating gripper members 212A, 212B shown in
A process to grab a handle of a basket or another food preparation item in accordance with an embodiment of the invention comprises the following steps:
1. Robotic kitchen assistant obtains estimate of basket handle in 6 DOF using sensors and knowledge of prior state.
2. Robotic kitchen assistant aims to capture the target slightly lower than the clamp plane to account for additional error. This is because of the way gripper mechanism is designed; z tolerance in the up-direction cuts into x and y tolerance because top of gripper diamond is larger.
3. Robotic kitchen assistant grabs handle. In embodiments, the gripper mechanism is designed to self-center and positive lock in same location given ˜0.75″×0.75″×0.5″ tolerances and +/−5 deg in orientations.
4. In embodiments, force feedback is used to validate that the gripper has solid grip on basket and confirm pickup and that the basket is not stuck on anything. In embodiments, the robotic kitchen assistant uses sensors to measure or monitor applicable forces. In embodiments, the robotic kitchen assistant uses sensors to measure motion of the basket relative to the gripper to improve the grabbing motion.
Agitator
In embodiments, the robotic kitchen assistant is operable to de-clump fried food so that the fried food may be cooked uniformly before final serving and in particular embodiments, an actuator assembly is operable to cause the fryer basket to shake and de-clump the fried food.
In embodiments, a robotic arm adapter assembly comprises a gripper and at least one lock actuator (not shown) to lock the position of the fryer basket relative to the robotic arm when the at least one lock actuator is activated. The gripper engages the fryer basket using a combination of bearing surfaces, springs, and/or flexures that provide low resistance to motion in a limited number of directions as described herein.
Additionally, the robotic arm adapter assembly can include a separate agitation actuator (e.g., component 280). When the basket is placed in the fryer by the robotic kitchen assistant, the lock actuator (not shown) is disengaged and the agitation actuator 280 shakes the basket. The low resistance to motion between the basket and the robotic arm prevents the agitator actuator from imparting significant forces on the robot arm, which could damage the robotic arm. Once the agitation is complete, the agitation actuator is stopped and the lock actuator engages and the basket is moved by the robotic arm.
In embodiments, and with reference to the agitator shown in
1. Robotic kitchen assistant identifies and localizes basket with respect to fryer.
2. Robotic kitchen assistant grabs basket handle with gripper 212. The action for the gripper arises from a first actuator in the gripper assembly 212.
3. Robotic kitchen assistant uses a second pneumatic or actuator 280 to impart momentum into the food particles through the basket.
4. Whether actuator 280 reaches end of stroke or basket hits back of fryer, the rapid acceleration/deceleration helps agitate the food in the basket to prevent from clumping together.
5. In embodiments, the robotic kitchen assistant uses sensors to evaluate the effectiveness of the agitation, e.g., whether food items were broken up. This could be carried out in various ways such as, for example, using a trained Neural Network in a similar fashion to the way the food type is classified, mentioned above.
The agitator 280 provides sufficient force to move food in the basket. However, in embodiments, dampening effects between gripper and robot ensure no large forces are imparted to the robot that could damage its mechanical components. Dampening can be accomplished in a number of ways such as, for example, by adding a dampening material in between the gripper and the arm. The dampening material acts as a shock absorber or cushion. The forces can also be adjusted by balancing deceleration to allow the food to be shaken but not enough to damage the robot. In embodiments using pneumatic actuators, the forces are adjusted by tuning the air pressure.
In another embodiment, one actuator is employed to both grab the handle or kitchen utensil, and to impart momentum into the food particles through the basket.
Latching
The systems described herein may be temporarily secured or mounted to the floor of the kitchen using a wide range of techniques including but not limited to: bolts with nuts or hand-tightenable fasteners, tongue and groove or dove-tail type mating features, ground mounted rails including brakes and locks to secure the robotic kitchen assistant in the desired position, floor mounted lathe or Longworth chuck to grip the stand of the robot, ground-mounted dogleg grooves to guide a peg on the robot stand into a secured releasable position, an enlarged or weighted ballast cabinet, spring-loaded BNC-like connectors.
Wheels, rollers, and other means to move the robotic kitchen assistant may be incorporated with any of the above latching configurations except where exclusive of one another.
The robotic kitchen assistant may be lifted using hydraulic jacks and hoists, cams, and wedges. Springs and other means to assist lifting the robotic kitchen assistant may be incorporated with any of the above latching configurations except where exclusive of one another.
With reference to
With reference to
Not shown, holes are drilled in the ground. Anchors are installed in the holes. The robot is aligned to match the holes in the plate 420 with the holes in the ground. Using the T-bolt wrench 440, the bolts are tightened. This design has the advantage of being moveable, and not leaving a foot print to trip on when moved, and is weighted down to minimize vibrations.
In the embodiment shown in
VI. Fryer Debris Removal
In embodiments described herein, the robotic kitchen assistant is operable to skim, remove, and dispose the food debris from the fryer.
With reference to
In addition to that described above, or alternatively, food debris may be removed by the skimmer 520 by blowing a gas across the screen. With reference to
In embodiments, and with reference to
Sill other techniques may be employed by the robotic kitchen assistant to automatically remove debris from the fryer including rapidly contacting the rim of a trash receptacle with the skimmer, or brushing the skimmer with a tool.
This claims priority to application Ser. No. 16/534,169, filed Aug. 7, 2019, and entitled “ROBOTIC KITCHEN ASSISTANT FOR FRYING INCLUDING AGITATOR ASSEMBLY FOR SHAKING UTENSIL”, which claims priority to application No. 62/717,725, filed Aug. 10, 2018, and entitled “ROBOTIC KITCHEN ASSISTANT FOR FRYING INCLUDING GRIPPER AND AGITATOR” and to application No. 62/757,601, filed Nov. 8, 2018, and entitled “ROBOTIC KITCHEN ASSISTANT FOR FRYING INCLUDING UNIVERSAL ADAPTER ASSEMBLY FOR GRIPPING AND SHAKING UTENSIL”, each of which is incorporated herein in its entirety for all purposes.
Number | Name | Date | Kind |
---|---|---|---|
2658444 | Wheeler | Nov 1953 | A |
4015494 | Spooner et al. | Apr 1977 | A |
4052319 | Friedman | Oct 1977 | A |
4545723 | Clark | Oct 1985 | A |
4853771 | Witriol et al. | Aug 1989 | A |
4919950 | Mak | Apr 1990 | A |
4922435 | Cahlander et al. | May 1990 | A |
5132914 | Cahlander et al. | Jul 1992 | A |
5285604 | Carlin | Feb 1994 | A |
5386762 | Gokey | Feb 1995 | A |
5389764 | Nishii et al. | Feb 1995 | A |
5458384 | Liu et al. | Oct 1995 | A |
5466025 | Mee | Nov 1995 | A |
5833295 | Farlow, Jr. | Nov 1998 | A |
5893051 | Tomohiro | Apr 1999 | A |
D412642 | King | Aug 1999 | S |
D492112 | Hardy | Jun 2004 | S |
7174830 | Dong | Feb 2007 | B1 |
7383963 | Svabek | Jun 2008 | B2 |
7920962 | D et al. | Apr 2011 | B2 |
7971450 | Furlanetto et al. | Jul 2011 | B2 |
8276505 | Buehler | Oct 2012 | B2 |
8610037 | Polt | Dec 2013 | B2 |
D702084 | Matos | Apr 2014 | S |
8820313 | Lutes | Sep 2014 | B1 |
9220371 | Demirakos | Dec 2015 | B1 |
9233470 | Bradski et al. | Jan 2016 | B1 |
9285589 | Osterhout et al. | Mar 2016 | B2 |
9483875 | Theimer et al. | Nov 2016 | B2 |
9538880 | Riefenstein | Jan 2017 | B2 |
9542621 | He et al. | Jan 2017 | B2 |
9785911 | Galluzzo et al. | Oct 2017 | B2 |
9815191 | Oleynik et al. | Nov 2017 | B2 |
10005184 | Gerio | Jun 2018 | B2 |
D825266 | Iorio | Aug 2018 | S |
10112771 | D'andrea et al. | Oct 2018 | B2 |
10154756 | Hall et al. | Dec 2018 | B2 |
10293488 | Hall et al. | May 2019 | B2 |
10422870 | Mindell | Sep 2019 | B2 |
10682765 | Mirkhaef et al. | Jun 2020 | B2 |
10919144 | Sinnet et al. | Feb 2021 | B2 |
11167421 | Sinnet | Nov 2021 | B2 |
11192258 | Sinnet | Dec 2021 | B2 |
11351673 | Zito | Jun 2022 | B2 |
11518044 | Liu | Dec 2022 | B2 |
11577401 | Sinnet | Feb 2023 | B2 |
20020028127 | Hart et al. | Mar 2002 | A1 |
20020082924 | Koether | Jun 2002 | A1 |
20040011321 | Hawaj | Jan 2004 | A1 |
20040111321 | Kargman | Jun 2004 | A1 |
20040154474 | Chan | Aug 2004 | A1 |
20040172380 | Zhang et al. | Sep 2004 | A1 |
20050036668 | McLennan et al. | Feb 2005 | A1 |
20050049940 | Tengler et al. | Mar 2005 | A1 |
20050193901 | Buehler | Sep 2005 | A1 |
20060278216 | Gagas et al. | Dec 2006 | A1 |
20070122000 | Venetianer et al. | May 2007 | A1 |
20080110347 | Wong | May 2008 | A1 |
20090192921 | Hicks | Jul 2009 | A1 |
20090210090 | Takemitsu et al. | Aug 2009 | A1 |
20090262206 | Park | Oct 2009 | A1 |
20100132692 | Shaffer | Jun 2010 | A1 |
20100182136 | Pryor | Jul 2010 | A1 |
20100296903 | Shah et al. | Nov 2010 | A1 |
20110153614 | Solomon | Jun 2011 | A1 |
20110264266 | Kock | Oct 2011 | A1 |
20120024170 | Fritz-Jung et al. | Feb 2012 | A1 |
20130033057 | Markham | Feb 2013 | A1 |
20130275236 | Koke et al. | Oct 2013 | A1 |
20130302483 | Riefenstein | Nov 2013 | A1 |
20140031978 | Takata | Jan 2014 | A1 |
20140062112 | Cho | Mar 2014 | A1 |
20140089299 | Kamei et al. | Mar 2014 | A1 |
20140157698 | Cihak et al. | Jun 2014 | A1 |
20140184496 | Gribetz et al. | Jul 2014 | A1 |
20140203012 | Corona et al. | Jul 2014 | A1 |
20140234066 | Mathi et al. | Aug 2014 | A1 |
20140324607 | Frehn et al. | Oct 2014 | A1 |
20140334691 | Cho et al. | Nov 2014 | A1 |
20140351068 | Renfroe | Nov 2014 | A1 |
20140363266 | Cooper | Dec 2014 | A1 |
20150019354 | Chan et al. | Jan 2015 | A1 |
20150290795 | Oleynik | Oct 2015 | A1 |
20150310624 | Bulan et al. | Oct 2015 | A1 |
20160037958 | Freymiller et al. | Feb 2016 | A1 |
20160078694 | Swift | Mar 2016 | A1 |
20160180546 | Kim et al. | Jun 2016 | A1 |
20160239705 | Masood et al. | Aug 2016 | A1 |
20160293470 | Oremus et al. | Oct 2016 | A1 |
20160307459 | Chestnut et al. | Oct 2016 | A1 |
20160327279 | Bhogal et al. | Nov 2016 | A1 |
20160327281 | Bhogal et al. | Nov 2016 | A1 |
20160334799 | D'Andrea et al. | Nov 2016 | A1 |
20170011319 | Elliot et al. | Jan 2017 | A1 |
20170024789 | Frehn et al. | Jan 2017 | A1 |
20170116661 | Sundaram | Apr 2017 | A1 |
20170130968 | Nagraj et al. | May 2017 | A1 |
20170154803 | Wang et al. | Jun 2017 | A1 |
20170169315 | Vaca et al. | Jun 2017 | A1 |
20170178070 | Wang et al. | Jun 2017 | A1 |
20170206431 | Sun et al. | Jul 2017 | A1 |
20170252922 | Levine et al. | Sep 2017 | A1 |
20170290345 | Garden et al. | Oct 2017 | A1 |
20170305015 | Krasny et al. | Oct 2017 | A1 |
20170348854 | Oleynik | Dec 2017 | A1 |
20170364073 | Guy | Dec 2017 | A1 |
20180150661 | Hall et al. | May 2018 | A1 |
20180339463 | Stone et al. | Nov 2018 | A1 |
20180345485 | Sinnet et al. | Dec 2018 | A1 |
20180365630 | Seals et al. | Dec 2018 | A1 |
20190256301 | Hashimoto et al. | Aug 2019 | A1 |
20190297899 | Weiss | Oct 2019 | A1 |
20190352028 | Mirkhaef et al. | Nov 2019 | A1 |
20190389082 | Higo | Dec 2019 | A1 |
20200009638 | Asada et al. | Jan 2020 | A1 |
20200030966 | Hasegawa et al. | Jan 2020 | A1 |
20200046168 | Sinnet et al. | Feb 2020 | A1 |
20200047349 | Sinnet et al. | Feb 2020 | A1 |
20200054175 | Roy et al. | Feb 2020 | A1 |
20200087069 | Johnson et al. | Mar 2020 | A1 |
20200121125 | Zito | Apr 2020 | A1 |
20200238534 | Goldberg | Jul 2020 | A1 |
20200254641 | Hocker et al. | Aug 2020 | A1 |
20210030199 | Olson et al. | Feb 2021 | A1 |
20210038025 | Almblad | Feb 2021 | A1 |
20210094188 | Rodionov et al. | Apr 2021 | A1 |
20210107724 | Cohen | Apr 2021 | A1 |
20210196081 | Kodali et al. | Jul 2021 | A1 |
20210208171 | Guarracina et al. | Jul 2021 | A1 |
20210276756 | Dunkel | Sep 2021 | A1 |
20210394371 | Ishizu et al. | Dec 2021 | A1 |
20220055225 | Sinnet et al. | Feb 2022 | A1 |
20220324119 | Kodali et al. | Oct 2022 | A1 |
20220346598 | Sinnet et al. | Nov 2022 | A1 |
Number | Date | Country |
---|---|---|
202014001807 | Mar 2014 | DE |
2547286 | Aug 2017 | GB |
2004062750 | Feb 2004 | JP |
2008296308 | Dec 2008 | JP |
2009106734 | May 2009 | JP |
2009297880 | Dec 2009 | JP |
4655912 | Mar 2011 | JP |
5814305 | Nov 2015 | JP |
0170087 | Sep 2001 | WO |
2006006624 | Jan 2006 | WO |
2012020858 | Feb 2012 | WO |
2015100958 | Jul 2015 | WO |
2015143800 | Oct 2015 | WO |
2016040361 | Mar 2016 | WO |
2015125017 | Jun 2016 | WO |
2016140622 | Sep 2016 | WO |
2017114014 | Jul 2017 | WO |
2017103682 | Aug 2017 | WO |
2018031489 | Feb 2018 | WO |
2018165038 | Sep 2018 | WO |
WO-2018165105 | Sep 2018 | WO |
WO-2019079345 | Apr 2019 | WO |
2022256799 | Dec 2022 | WO |
Entry |
---|
16534169 Robot Frying Basket001 (Year: 2018). |
B. Siciliano & 0. Khatib, Handbook of Robotics, published by Springer-Verlag Berlin (2008). |
Beucher, Serge, and Fernand Meyer. “The morphological approach to segmentation: the watershed transformation.” Optical Engineering—New Yark—Marcel Dekker Incorporated—34 (1992): 433-433. |
Bonanni et al., “Counterintelligence: Augmented Reality Kitchen”, CHI 2005, (Apr. 2, 2005), URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.88.2875, (Jun. 12, 2018), XP055559956. |
Goodfellow et al., Generative adversarial networks, Communications of the ACM, vol. 63, Issue 11, Nov. 2020 pp. 139-144. |
International Preliminary Examination Report dated Jul. 20, 2018 for PCT/US2018/021066. |
International Search Report and Written Opinion of ISA dated Aug. 17, 2022 for PCT/US2022/072666. |
International Search Report and Written Opinion of ISA dated Jul. 11, 2018 for PCT/US2018/020948. |
International Search Report and Written Opinion of ISA of PCT application No. PCT/US2022/071871 dated Sep. 1, 2022. |
Ju Yong Chang, Haesol Park, in Kyu Park, Kyoung Mu Lee, Sang Uk Lee, GPU-friendly multi-view stereo reconstruction using surfel representation and graph cuts, Computer Vision and Image Understanding, vol. 115, Issue 5, 2011, pp. 620-634. |
Kaiming He, Georgia Gkioxari, Piotr Dollar, and Ross B. Girshick, Mask R-CNN, arXiv, 2017. |
Krystal B., The magic of Eatsa, explained, (Mar. 2, 2017), URL: https://www.washingtonpost. com/ . . . 017/03/02/57c95fb0-f55a-11e6-b9c9-e83fce42fb61_story.h tml?horedirect=on&utm_term =. 1 08e357 d67 df, (May 21, 2018). |
Lin, Tsung-Yi, et al. “Focal loss for dense object detection.” Proceedings of the IEEE international conference on computer vision. 2017. |
Lucas, Bruce D., and Takeo Kanade. “An iterative image registration technique with an application to stereo vision.” (1981): 674-679. |
Ohta, Yu-Ichi, Takeo Kanade, and Toshiyuki Sakai. “Color information for region segmentation.” Computer graphics and image processing 13.3 (1980): 222-241. |
Paul Viola, et al., Rapid Object Detection using a Boosted Cascade of Simple Features, Accepted Conference on Computer Vision and Pattern Recognition 2001 (https://www.cs.cmu.edu/˜efros/courses/LBMV07/Papers/viola-cvpr-01.pdf). |
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks Faster”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39 Issue 6, Jun. 2017. |
Simon, Dan. “Kalman filtering.” Embedded systems programming 14.6 (2001): 72-79. |
Xiao-Shan Gao et al. “Complete solution classification for the perspective-three-point problem,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, No. 8, pp. 930-943, Aug. 2003. |
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Child | 17517907 | US |