So thank you very much for being here, Yadu, and let's hear about it. All right. Thank you very much, Marco, for that warm introduction. A very good morning, good afternoon, good evening to everyone here. My name is Yadunan from Open Robotics, and today I'm really excited to talk to you about this massive open source project we've been working on at Open Robotics. It's called OpenRMF, but specifically I'm keen to share a bit more about a practical deployment of a multi-fleet task allocation system that we've developed. And this is a solution to the classic problem of how do we distribute a set of tasks across a set of robots, given that each robot is from a different brand, may have different capabilities and different constraints. So that's going to be the objective of this talk, and I hope you guys are excited too. With that, let's get into it. So what this talk will cover are three broad aspects. We'll begin by exploring the need for interoperability, and interoperability is just a fancy word for saying different systems can talk to the same system and can also talk to each other. So this could be different brands of robots that need to talk to each other or robots that need to talk to an elevator in a building, etc. And secondly, we'll talk about how the robotics middleware framework addresses some of these interoperable challenges. And finally, we'll talk about how we can solve this problem of allocating tasks across different brands of robots. Okay, so what is this interoperability problem? So nowadays, we're seeing facility owners increasingly adopt robotic automation in their sites. So if you're a hospital owner, or a airport owner, or a hospital or a hotel owner, for example, you may want to introduce robotic automation into your facility. So you might get a brand of cleaning robot to do some cleaning, a delivery robot to do some deliveries, maybe a security robot for patrolling. But the downside is that all these robots typically will come from different vendors, because a robot that's very good at cleaning will be made by a different company than the one that makes delivery robots. So inherently, when we bring these different robots into the same facility, we're introducing challenges for the operators and facility owners, primarily because these robots don't communicate with each other. They're made by different vendors, so they use different communication protocols and messaging systems. And as a result, it could lead to a lot of problems in deployment and also some operational challenges. So one common challenge that arises is you have two or more robots that go down the same hallway. So if you can imagine a hotel or a hospital corridor, which is pretty narrow, if you have two robots coming in the opposite directions, they'll think of each other as an obstacle and kind of do this robot dance to try to go around the other robot. But they'll just remain stuck in the end because they can't talk to each other and say, hey, give me a way or let's take a different path. For example, another complication from this is getting these robots to talk to the same building systems such as doors and lifts. So typically what happens is if you have a lift in your facility that you want a robot to take the robot vendor will come in and do a lift integration for their specific robot, which the second robot feed cannot really use because it uses a different messaging format. So in this case, when you want to introduce a second robotic fleet, you need to do a second integration. And these integrations are not really cheap. So the cost really adds up as you scale robotic deployment. So yeah, and moreover, from a practical standpoint, if you look at operators who need to use these robots to send out to send out robots that dispatch certain tasks. It's not really an easy job because maybe the task, the requirement is very simple, like I need something to be delivered from here to there. But if you have multiple feeds in your facility, the person making that decision goes through quite a bit of process they need to first log into the different dashboards that come with each robot vendor. So for example, we could have vendor a has his own dashboard vendor B has their own dashboard and from from that the operator would need to log into these different dashboards and see which robots are available. And from that decide which one has sufficient battery to do this task, and then finally use that dashboard to send the task to that robot. So some of these robots may be ready to deploy right now some may have low batteries some can't do deliveries for example they could be cleaning robots. So the decision making process is extremely manual. Sorry, Marco, I got a notification saying the screen sharing stopped. Are you able to see it? Yeah, can you try resharing? Sure, let me try that. Are you seeing the screens now? I'm sorry, are you seeing the slide? No, we also lost your video feed. Your video feed is up, which is missing your slides. All right, let me try that again. Yeah, I think it's charging up. Please go ahead. Sorry about that. Okay, so we were talking about how the process of submitting a task can be really manual and involves a lot of decision making when you have multiple robots. And the solution I want to talk about today is open RMF or the robotics middleware framework. So this is a fully open source platform that we've developed that opened robotics. And the goal here is to really provide a common language for robots to interoperate. But apart from that, we provide a bunch of tools and libraries to do a bunch of different functionalities we'll talk about in the next slide. But some of the takeaways are this is fully open source under Apache 2.0 license. It's actually be maintained by open robotics. We have tons of community collaborators. And we provide out of the box integrations for several robotic fleets doors and other lift systems. So what does RMF do? It's very good at a few things. It does global traffic management. So this is what we're seeing on the top left corner here. So once we have different fleets integrated with RMF in the same facility. It's continuously monitoring where these fleets are trying to go. It can be planned the routes for these robots if there's a conflict and it will avoid situations where there's two robots in the same hallway, for example. Secondly, you can have RMF communicate with other automation systems you have in the building already. So you may have a robot arm that dispenses beverages. You could have a trade return system, for example, and then you can you can integrate these systems with RMF and have robots automatically interact with them to accomplish tasks. Another big capability of RMF is it allows these different fleets to share the same lifts and doors. We talked about the challenge with multiple integrations previously. Whereas now you would do a one-time integration with RMF for every door and lift in the building. And anytime you introduce a new fleet, it can readily use that same door or lift. And lastly, you can do intelligent task allocation. So this is a functionality where you can just submit a bunch of tasks which have semantic meaning. And these tasks will automatically get allocated to different robots and RMF will ensure that they get executed. And this is going to be the focus of today's talk, basically, how we accomplish this. Before we get into that, just a quick note on how RMF interacts with vendor robots. So you can see, as you can see here, you can have different brands of robotic fleets in your facility. Typically, each of these robot fleets will come with their own API. So a vendor will provide an endpoint for you to connect to their system could be the robot directly a cloud server. So once you connect to that system, you can issue commands to the robots and receive information from the robots. So through these APIs, you typically need to do a one-time integration, what we call the smart fleet adapter. And this adapter is basically a bridge between the RMF ecosystem and the vendor-specific software. So again, these fleet adapters have certain APIs that we provide in RMF. So the goal is for you to use these APIs and take the endpoints from the vendors and combine them into an adapter to bridge the communication gap. So once you do this, you can consider your fleet to be RMF integrated. And with that, we're able to get some information from the fleets and we're also able to send information to the fleets. Okay, so with that in mind, let's examine this problem statement that we have, which is multi-feet task allocation. So it can be summarized as given M number of tasks with varying start times and certain descriptions. So it could be a cleaning task, delivery tasks, etc. And then we have some number of robots across F different types of fleets. How can we distribute these M tasks across these F fleets, such that robots are only assigned the tasks that are capable of performing. So this means that the delivery tasks should not be assigned to a cleaning robot. And further, the robot should have sufficient resources to perform all these assigned tasks. So if it's a cleaning task, you have to check that the robot has sufficient battery and cleaning resources to perform the task. And lastly, and most importantly, we need to ensure there's some kind of optimality in the assignments because that's really the goal of being better than the human operator. We want some kind of optimality in these assignments. So there are certain assumptions we make. We assume that each task can only be executed by one robot. So this means that if it's a delivery task, the same robot will do the pickup and the drop off. It's not going to hand over the payload to another robot to continue on with the task. Secondly, a robot will only perform a task once it's finished completing the previous task. And lastly, we have some constraints on charging resources as well. So when we first tackle this problem, we did what everyone normally does a literature review of some of the algorithms and solutions that exist for multi robot task allocation. So while we found several solutions for multiple robots, I think we were in a unique position where we're dealing with multiple fleets. So we were able to synthesize some of the information from all of this literature and put it together in our own implementation. So some of the big takeaways from these literature review. Sorry, Mark, did the slideshare stop again? Yeah, I was going to say that. Yeah, the slideshare stop again and the video. So if you can just, oh boy. Yeah, sorry about that. Sorry, it's a backup. Yeah, everything's working. Cool. So yeah, we borrowed heavily from a lot of literature that talked about multi robot allocation. We extended this to multi fleet task allocation. And some of these approaches that we borrowed is the auctioning system. So what this means is that every fleet is basically an auctioneer so they will bid for a certain task. And let me explain how the system works.  And this is kind of a central authority that maintains all the tasks that are received. And it's also responsible for talking to the different fleet adapters. And if you remember from the previous slide of fleet adapters basically that bridge between the vendor fleet and the rest of RMF. So once these requests are received by the dispatcher, the dispatcher will then send out bids to each of these fleets. So these happen through some internal communications over Ross to and the middle where we use and Ross to his DS. So with the DS, it comes a lot of security and encryption, and a lot of great functionalities. So we're using protocol as DDS for these communications, but this dispatcher node will then let each of these fleets know that hey, I have a request to perform a delivery task. Something needs to be picked up here and dropped off there. And then each of these fleet adapters, knowing the capabilities of their robots will respond with a with a proposal. We receive a bid notice from the dispatcher and then we submit back a proposal and this proposal typically includes information on whether you can perform the task or not. And if you can perform the task which robot you're going to sign it to and when will be completed. So the proposal is received. The dispatcher node, then goes through all the proposals within it for certain time window and then decides which fleet to award the task to. And this criteria to award the task can be configured. If you prefer having fastest completion time you can set that as the criteria or the metric. If you prefer overall completion time. That's also a metric you can use, or if you want to do something like first in first out. That's also possible. It's a very flexible selection system. So the great approach with this is that it's a fully distributed system. You don't have a single node that's aware of all the capabilities of all the fleets. This responsibility is offloaded to the fleet adapter. And another great part of decentralized approaches is it's easy to add on another fleet. You don't need to change anything in the system. So I would just add another fleet adapter that has maybe, I don't know, drones or something. And that's able to respond to the same tasks without changing anything else in the system. So this is the approach we're going with it's a distributed multi fleet task allocation system. And one detail that I left out is how does this fleet adapter decide what is the best robot to allocate the task to. So we said that a task comes in, it goes to each of these fleets and each fleet tries to accommodate this task in the best possible way and then submit a proposal back. But how does each fleet decide how to best accommodate the task. And in order to do this, we've developed this new planner, which is a task allocation planner. It's a simple a star solver with a lot of constraints defined into it. So we have semantic information captured about what the task is, what the state of a robot is.  I won't go into the details of the algorithm in this talk. But it's just an approach where we, where we prune different solutions and try to find the best optimal assignments for the set of robots. So for example, if a fleet adapter is running this a star solver, it's going to have information on all the states of the robots. So if it has three robots, it knows what the battery level and starting position of each robot is. It will try to compute the finished position at battery level for each robot and then decide which robot to allocate the task to. So what's happening here is we're optimally allocating the task within each fleet. So each fleet does that, and then the optimal assignment for each fleet is submitted to the dispatcher node and the dispatcher node then picks an overall solution. So for ensuring optimality here, and then if we can ensure optimality at this level at the dispatcher level, then we can ensure some kind of global optimality, optimality, optimality among the assignments. So yeah, this this approach is not entirely useful without accounting for battery drain. So for example, initial states of robots, maybe too low to perform the first task. So maybe you have one robot in a fleet and it has 20% battery level whereas you need more than 30 to perform this task. Robots we need to charge in between tasks. And how do we automatically instruct robots to go to the chargers in between tasks. So the way we chose to tackle this problem is build in a battery modeling framework within the task allocation planner. So, so I thought I lost the connection there for a moment. So once we can estimate what is the battery drain for each task, we can then predict whether robots will need to charge in between tasks or whether they need to charge before they start performing the task. So that's what we've done with RMS. Again, with a lot of literature review, we try to study how battery drain for robots can be modeled. We understood that modeling this train is extremely complicated because it depends highly on battery chemistry. The state of the battery, the age of the battery and a lot of other parameters. And we don't want to be too rigid in what kind of battery models you can use. So now we have a kind of library that users can plug in play if they want to use our simple battery drain model. But if they have more complicated models machine learning based or some other data driven models, they can plug those in as well. So with these with this battery model plugged into the task allocation system. Here's a very raw output of our task allocation planner. And as you can see, if you could give it a set of agents or robots with certain IDs, they're starting locations and they're charging waypoints. And if you give them a set of tasks to perform where you need to pick something off at a certain grid location, drop it off at another grid location. It will output a set of assignments for each agent and automatically include any recharging tasks if required again for details on how this algorithm works. It's out of the scope of this talk, but we could take those questions offline. But I'll just talk about some other quick capabilities we can do with the task allocation planner. We can have priorities for tasks so we could assign higher or lower priorities to certain tasks. And internally in the task allocation planner, we can make sure that high priority tasks are completed before the low priority ones. Another thing we can do is automatically add tasks in between. So we already talked about injecting recharging tasks for robots, but we can also ask each robot to go to a specific waypoint or a parking spot after it performs all of its tasks. So yeah, this is kind of what the final results look like. So this is running RMF in a simulation environment here. We have three different feeds of robots and this is our RMF dashboard.  And then the user can just hit submit tasks and all of these tasks get automatically assigned to the different feeds and you can see. As you can see in the video here, some of these are delivery tasks. Some of these are cleaning tasks that get assigned to different robots. All right. It's just a quick video of it running on real robots. We just don't do things only in simulation things work in real life too. So here we have a cleaning robot and not a patrolling robot that are kind of moving around performing tasks which are controlled by RMF. All right. So there are a lot of topics covered in this short talk. All of the source code has all of the source code is available for open source. It's available on GitHub. These are the relevant repositories. We have our battery for the battery modeling our task for the task allocation. And we welcome everyone to try out some of the scenarios in simulation from the arm of demos repository. If you have any questions, please feel free to open a discussion at this link. We're super active in developing and responding to discussions as well. With that, I'm happy to take any questions.


Oh, I slept so well in this bed last night. I got to cuddle somebody very special, not named John. Hi, Mira. Oh, let's see what the world looks like today. Oh yeah, look at that sunrise. Oh, I can't tell you how nice it was to have a room that was warm away from the rain and some restaurant food and just some time on the internet, chatting with friends, a little connection to the outside world. But now it's time to head back out into the sticks. You ready, John? I am. No crashes. No flaties. No whammies. Whammies, that's a Canadian accent. By the way, you'll always know he's Canadian because he's got the flag. Mira, it's time to go. Yeah, get that tail wagon. Let's go, let's go ride bikes. Yeah. So John has an accessory most cyclists don't have. Yeah, so this is a roughware leash, joggers use them all the time. I just better drive my waist there. And then, hey, Mira, I see you, baby. Oh yeah. Yeah, exactly. And then, yeah, just clip it in so I can help lift her up or lower her down when she jumps into the box. So she just hit the ground too hard. Those poor paws get a lot of work. Bikes out of the hotel. It's one of the best roadside hotels I've ever stayed in.  I'm very, very, very ready. Thank you Lima, Montana. I call it Lima. I think the locals call it Lima. This is the bar we ate out at last night. It was so good, Pete Bar. What are we doing today? What are we doing today? I think it was closed. Oh yeah, you do have a new shirt on. You know that smell, that fresh smell of the world after a rainstorm? That's what's going on right now. Mixed in with the scent of cut grass in these farms and the dirt road. I love how quickly you can go from a highway town with semis rushing by to five miles later and you're in the middle of nowhere again. And it's silent and beautiful. And all you can hear is the sound of the birds chirping and the cows mooing. Morning, cow. Getting over here on the side road because the main road is seriously muddy. We're a wolf pack now. We've been riding through pasture land pretty much all morning and all of a sudden there's a big patch of these purple lupines. Wow, it smells good and it looks good too. There goes Mira off to the races. You can tell that it rained a lot out this way. This road right here is just a slop factory. Mira, what are you doing in the mud? What are you doing in the mud? You're not sleeping with me tonight. Mira, nice and cool in there, huh buddy? So Bjorn, you said you're 24. How did you convince your mom that this was a good idea because I'm 40 and she still struggles with it? I told her to support me rather than not. And then she liked that idea a lot more so. Oh, that's good. How is she supporting you? Emotionally and let me call her a bunch also, vice versa, so I get to support her while I'm out here and calling her a lot.  As much as I have reception. So whenever I have reception, I do give her a call. Sometimes multiple times a day. Oh, you're my kind of guy.  You a mama's boy? Yes and no. Yes and no, okay. Well, I proudly admit that I'm a mama's boy.  Hi Mira, you are really muddy. Don't tell mom, don't. Okay, whatever. I'm already dirty.  Marin. Marin, this is a gravel bike. Yes. What's skinnier tires? A little tougher in the mud, but faster on the flats. Yep, yep. Great morning so far. Pushing through mud and through the cow pastures and just riding bikes. So I've got it all the way unengaged So here we are riding one way. We get some northbounders coming at us and they need some help. And John is a very good mechanic. So we're stopping and helping them out. Yeah, John. What's up? How long you been on the trail? Couple weeks, I forget. You loving it? It's blending, yeah, it's outstanding. It's great out here. I just met up with these two a little while ago. Oh yeah? I'm riding solo and they're riding together. No way. And then, so what we'll do is we'll get them close to see how that is. Okay. And so we can adjust the whole caliper. So these two started at the border of Mexico in New Mexico. What are we in for? Don't go to New Mexico. What's the run with New Mexico? A lot of heat, 95 degrees, no clouds, no trees. But have you been loving this experience? Yeah, it's pretty great. All right, the moment of truth. How did you do that? I've been dickin' on this thing for a day, bud. Yeah! Yeah, he's so happy. You're the hero. John. Yeah, John. Thanks, bud. I appreciate it. We will now wish you well on your way into the deep woods of Montana as we go south. All the best things that I've seen And all the most wonderful views All the best sights that I've seen Yeah, I've seen with you All the best nights I never slept All the days I'm holding on to All the best times that I've had Today has definitely been the easiest pedaling so far on this route. Completely flat, a couple little bumps here and there. And today we have a tailwind and I feel like we deserve it after yesterday, after all the headwinds and then the storm. And when you have those tough days, you really appreciate the easier ones like right now. I'm just sitting here daydreaming, looking at these clouds. La, la, la. I'm in la, la, land. So today has been incredible. We've run into like 10 Tour Divide riders. We just ran into these two dudes with Southern accents. Who are you guys? Where are you coming from? Brett McCormick, Epic Diabetic. We started at Antelope Wells. Oh, that's the border, right? Yes. How's the trip been? It's been very eventful. I'm so glad to get to meet you. And get to meet you and you. And this is my sidekick. Charlie McBride. I've got a Facebook McBride in the divide. And so it's, we've been just kicking it since June 20th. Wow. What's been your favorite part about all this? The food and the people. People we meet in the food. Yeah, it's been pretty epic. Have you ever done something like this before? We did the Rat Thousand, the Race Across Texas for a thousand miles last fall. And so, but that's still, that's supposed to be kind of preparatory for this. It's not. Is this harder than you thought it would be? It is major harder, especially in Mexico. Hang tight on that one. You got to rely on the trail angels a little bit.  And so just go out there and have faith in yourself. And like he and I are saying all the time you get out there and you figure it out as you go. I'm so glad to meet you. Save you man. Let it get after it. Love it, love it. Bye. Those guys were awesome. They had great energy.  It's only been riding bikes for two years and looking and riding the divide. We are in our last two or so miles of Montana. And I just want to give a shout out to Montana. Thank you. It's been so great here. The only thing that we ask when people stay with us is that they pay it forward. We'd like to think that we have four acres of love right here and kindness. And we just hope that any of that love and kindness we just spread out into the world. So that's why we do it. That's why we do this. What a stage. Now we're going to head into Idaho for a teeny little bit. And then Wyoming. Are you guys ready? I'm ready. I'm ready. Idaho. Idaho. Idaho. Thank you, Montana. Hello, cows. With all the excitement of crossing the border and having a nice fast downhill into Idaho, I thought I should stop and just take it in because these mountains over here are stunning. And as I'm looking over here, I see a big globe on top of one of the mountains. It must be an alien watch tower. That's the only explanation. Look at that thing up there. It's like, wow. I've never seen anything like that at the top of a mountain. That's pretty cool. It's probably some sort of a observatory. What do you think, John? That's nice to be back in Idaho. Yeah. Good little descent. Nice, it's a nice welcome. That was great. We have found a nice spot here in the woods at the end of a road. My tent is actually in the middle of it. I hope I don't get run over by an ATV tonight. It is Friday. There's a lot of campers in this area. It is beautiful. And yeah, another great day. Really fun to have crossed into Idaho. I didn't know we would be crossing into a state today. And then tomorrow we're gonna go into Wyoming. It is time you've been waiting all day for the stick. Boom! Oh, John, I have our beans. That's what we're gonna dip the beans in. Yes. The gas station today did not have tortillas. And we were like, what are we gonna put beans on? So John brought this bag of chips and thoroughly crushed them. Oh, come on. Oh, come on. Is there a few in here? Oh, actually, they look pretty good. Yeah. They look pretty good. Just pour the beans right in there. Yeah, let's make a cereal. Bean cereal. Yum, yum, yum, yum, yum, yum, yum, yum, yum. Here's a little tent talk to end the day. And I gotta say the highlight of today was meeting the other riders and hearing their stories. I really love the two guys from Texas. They were so psyched and positive. And they had been through some tough stuff, crashing and recovering and healing their bodies. And they were all smiles as they were riding into a headwind when we saw them. The view out of my tent is always beautiful and it changes every night. And this is a funny story. When I get back to my house after long trips, I have a hard time sleeping indoors because I love the fresh air. And when I'm enclosed with four walls and a ceiling, it's hard for me to adapt to that. I really, really love being out here in the wild. And it's been a wild week so far. I've had so much fun. I've met already a lot of great people and there's so much more down the road. And I'm very thankful and grateful for every pedal stroke that I get to do. My body's feeling great and this is exactly what I had hoped it would be. Magical. Buenas noches. Or buenas nachos. I love nachos. I wish I had some nachos right now. Mira. Come on. There you are. Oh, good way to start the day.


When you watch a baseball game on TV one guy that they will pay a lot of attention to is the pitcher and This is for good reason as he's arguably the most important player on the field But many of the things that pitchers do are unique to baseball So let's look at a general overview of pitchers and how their jobs work Like what pitches they throw how they know when to throw each type of pitch and finally we'll look at a few different types of pitchers that there are in baseball The pitchers job is to make it as difficult for the batter as possible to hit the ball hard or even to hit it at all While not getting too wild and either walking or hitting the batter The main way that the pitchers going to do this is by gripping the ball differently Which will allow his pitches to be different speeds and it will also cause them to move differently on their way to the plate I'm not going to get into how each pitch is actually gripped you're better off learning that from an actual pitcher But there are no shortage of websites and videos out there that will explain just that More important for us is what the pitches do The simplest type of pitch is the fastball or more specifically the foreseam fastball It won't move at all. So as far as our batter will see it will come straight in But it is the fastest pitch that a pitcher can throw and when I say fast in Major League Baseball Which is the highest level of play in the United States? pitchers fastballs will usually be somewhere between 90 and 100 miles an hour and keep in mind that the pitcher is only 60 feet six inches from the batter Similar to the foreseam is the two seam fastball which will be a few miles an hour slower But it will also have movement meaning that the pitcher will grip the ball in such a way that it will drop towards the ground quicker than the foreseam so to a batter it will start up high and then it will drop down to here hitting a ball coming in at 90 miles an hour is tough enough, but now it's moving and in the two seamers case It is sinking Similar to a two seam, but usually a little slower There is another type of pitch called a sinker which will have similar movement The other kind of fastball we'll look at is the cut fastball or the cutter Which rather than sinking will cut sideways which way it goes will depend on whether the pitcher is right or left-handed It will also affect the batter differently depending whether he is hitting left or right-handed in this case Our pitcher would be right-handed and our batter is left-handed. So it is moving in towards the batter In addition to fastballs we have off-speed pitches, which will be slower But still be tough to hit because they will have a lot more movement on them One of the most common off-speed pitches is the curveball Which will be similar to a sinker in that it will get partway to the plate and then have the bottom drop out a Combination between the curveball and the fastball is a pitch called the slider Which will be in the mid to low 80s in terms of miles an hour So not quite as slow as the curveball But you're still going to get a lot of horizontal movement on the pitch Sliders and curveballs are also called breaking balls because they drop or break so much The batter will obviously not know which pitch is coming toward him So he will try to pick up on things like the pitchers armspeed to try and get an idea of which pitch is Being thrown but pitchers have used this to work against hitters by creating a pitch called the change-up Which will feature quick arm speed and not a lot of movement as if he's throwing a fastball Only the pitch will come in at 75 miles an hour instead of 90 Which will obviously wreak havoc on the hitters timing The last pitch we'll look at is one you won't see all that often that some pitchers have been successful By mastering something called a knuckleball Using a knuckleball will result in a pitch that is very slow, but virtually unpredictable in where it will go Even when he lets it go the pitcher won't know which direction the ball is going to go Some batters have actually said that the pitch looks like it gets halfway to the plate and then goes up But that's not quite how physics works Most pitchers will throw three or four of these pitches There's a balance between having a large variety of pitches to go to and being able to throw each one of them well Some pitchers are able to have success with five pitches But other pitchers like knuckleballers will have success throwing just one or two types of pitches almost exclusively This will also depend on a pitchers style Power pitchers tend to focus more on speed Whereas control or finesse pitchers will focus more on throwing his pitches accurately as possible Okay, so we know that pitchers want to throw off batters with their different pitch types But with pitches coming in so fast and in such varied ways. How is it that they do not throw the catcher off too? Well before every pitch you will see the pitcher pause and look in at the catcher Who will be crouched down behind the batter and the catcher will put down a certain number of fingers This is called the sign or the signal Each number means a different pitch So if the catcher wants him to throw a fastball he would put down one finger if he wants a curve ball He would put down two Sometimes it is the catcher who is picking which pitches to throw sometimes that will be the coach from the dugout Who was giving signs to the catcher to relay to the pitcher? Ultimately though the pitcher and the catcher are in this together So if the pitcher does not want to throw a pitch that the catcher suggests You will see him shake his head and the catcher will put down a different number of fingers to pick a different pitch With such a diverse number of pitches One of the worst things that can happen is that signs get crossed up and a pitcher throws one type of pitch While the catcher is expecting another not only could that cost their team runs But it could be dangerous to the catcher and the umpire Luckily though, you won't see that happen too often If a runner gets on base especially on second base He will be able to see and potentially steal which signs the catcher is giving So the catcher may put down a series of signs like he'll hold down three fingers and then one and then two The pitcher and the catcher will decide in advance Which is the real signs? Maybe it's the first one that he puts down so they will know what the real sign is But it will make it a lot tougher for the runner to pick up on Finally, let's take a look at pitcher rolls We already mentioned power and finesse pitchers which refers to their style But more often you will hear them referred to in terms of when during a game they will pitch All pitchers are eligible to pitch at any point during a game But over time they have become more specialized in where they enter the game The first type of pitcher is the starter who as you may have guessed will be the first pitcher In the game for his team starters are usually the best pitchers on the team and major league teams will usually have five starting pitchers That group of their starters is called their starting rotation Because of that starters will only throw once every five days And they will usually stay in the game for as long as they can But if they make it through at least five or six innings without allowing too many runs It will be seen as a good outing for them Once the starter comes out of the game a reliever will take his place Relief pitchers are any pitchers who are not starters If the starter allows a bunch of runs early in the game a long reliever might come in This is a guy who's capable of lasting most of the game, but isn't quite good enough to be a starter Other pitchers will be middle relievers that will come in for one or two innings at a time Although some managers won't even leave them in for that long depending on the situation Most teams now will also have a set up man Who if the team is winning will throw in the eighth inning And a closer who will throw the ninth inning to close things out These two guys are usually pretty good, but they rarely throw more than one inning per game Pitching can be a confusing topic if you were just getting into baseball But the good thing is that with so many pitches thrown during a game You will have the chance to see them look in for the sign And deliver their different pitch types a few hundred times per game So you should be able to catch on pretty quickly


Because this is one of the most requested chairs we have been asked to review, it deserves more than just a rushed review based on a single month's experience. In this video, I give you my final thoughts on the Sihu M57, how it compares to a gaming chair, how it compares to a mesh chair within the same price range, and how it compares to a 80,000 pesos chair. I'm Rafael from Hardware Sugar, and this is the ultimate review of the Sihu M57 after three months of use. What's up Internet? It's still hard to get any PC parts today, but that doesn't mean you can't get this shirt to remind you of what you still need. Jokes aside, this is true. We made our very first merch to not only reflect our passion for PC building, but to help you teach your friends what you know through the Hardware Sugar PC Anatomy shirt. To order, go to our site or click on the link in the video description. Hover over products and select T-shirts. We have three designs and they come in sizes ranging from small to XL. Press add to cart, select your delivery option, and your payment terms. When we last talked about the Sihu M57, we had just finished putting it together and talked about my first impressions of it. You can check that video out here above. Something which I highly recommend because you can get to see the ease of assembly and the quality of the parts up close before they get hidden away from the chair when it's fully assembled. During those three months of testing the M57, I switched back to my Herman Miller Aeron chair once a week.  Before we go any further, I want to give a special shout out to TW for sending us a Sihu M57 for review and for correcting us that the squeak scene in our original video was because of improper assembly on my part. I forgot to put the washers. My bad, everyone. I can happily verify that there is no squeaking sound whatsoever when I sit on it now. No more sound. This video is not sponsored by TW in any way. However, numerous subscribers to our channel have voiced out support for TW's customer service, something which is important to emphasize in this day and age of online shopping just in case something goes missing or needs fixing. Let's talk design. In terms of build quality, the M57 looks and feels as premium as it does based on the pictures and videos I've seen of it online. Is able to achieve this through three subtle design elements which are the choice of metal on plastic, finish and quality of the plastic itself, and design quality of the mesh. First, the dash of metal invokes a sense of strength and quality. As I was assembling the chair, the bars were quite heavy for a small piece of metal and you could probably kill someone with this if you use that as a weapon in the proper way. Hints of metal remain on other parts of the chair as well such as the lumber support dial thus creating a sense of design continuity. Metal is a good way of making a product appear more expensive than it actually is and mere hints of it is enough to give off that appeal. This is unlike most gaming chairs which more or less stick to the static plastic. Second, the finish of the plastic is both smooth to the touch and has a clean matted finish. Cheaper or poorly made chairs often have these splinter imperfections which stick out. This is not the case for the M57. While a good part of the base of the chair is made of plastic, it is not something which makes it cheap looking. Looking at the M57 from afar and upon touch, the plastic finish is subdued and minimalist especially if you get the white version we have. For around the same price, my favorite budget office chair, the Ergo Dynamic a review of which I'll link up above is definitely made of less premium plastic material compared to that of the M57. Even when the Ergo Dynamic was new, it never felt this nice and smooth. Third, the mesh is both bouncy and sturdy. This is the ideal flexibility you want in a mesh chair seat primarily because your seat will last longer similar to how buildings are made to withstand earthquakes by making the buildings move alongside it. Or in this case, your mesh moving alongside the weight of your butt when you sit on it.  This isn't true. The quality of mesh varies from chair to chair. Even the assembly of how the mesh is assembled varies. You won't find staples keeping the M57 together unlike the Ethan chair I reviewed. This stitch is professionally done and is actually a little more sturdy than I want it to be. Also, see who gets extra points for creating a modern art skin embedded onto the mesh itself. This is something which most reviewers of the chair have left out completely mostly because it adds a nice yet subtle touch which most won't even notice unless they're a chair freak like me. Mesh chairs also provide a lot of natural ventilation.  It is quite an experience to feel the flowing of air when you leave an electric fan on pointed at you. I am a big advocate of mesh chairs over leatherette simply because leatherette is ultimately self-defeating. Leatherette peels after a year or so because of oil secreted from your body. But because the material itself is not breathable, it actually encourages you to sweat and produce the very oil which begins the rupturing process. In short, if you're looking for a long-lasting and comfortable chair, go with mesh, fabric, or a combination of both. The ergonomic is an example of a combination of both. The M57 is also a lot easier to keep clean because if you spill a drink, most of that goes through the gaps and if you're worried about crumbs, wiping them off from the top would allow the rest to fall between the gaps as well. Alright, moving on to ergonomics and comfort. The M57 offers all the correct adjustable parts. So yes, the arms go up and down, move from side to side. There's a headdress which goes up and down and can be tilted to several angles. The chair's height can be adjusted. You can recline the chair and lock the tilt function in place. There's also a lumbar support and these are all good things to have and you should be concerned if your current chair doesn't offer them. Unfortunately, while the chair might appear to have what you need, that doesn't mean they were designed correctly. The headdress has been the most difficult of all the pieces to pin down. Sometimes I look for it and sometimes I frustratingly remove it altogether. After I find a comfortable position I like, I end up changing my mind later on when I come back and so I have went through a lot of wasted time so far adjusting and readjusting the headdress over and over. I thought that I would be able to find my ideal position after three months of use. However, I never really did. It is fortunate the chair looks nice even without it on and so if you encounter the same issues I did, just decapitate it. Reclining with a headdress and putting my feet up is one of the most blissful experiences I encountered with the ergonodynamic. While the MT-7 does recline, it does not recline as far back as I wanted to. My ergonodynamic while it being eight years old still maintains fantastic build structure and I never thought the back would snap off. The MT-7's recline is good but not amazing. If it reclined just a few more degrees then I would probably recommend this chair over the ergonodynamic. Right now, however, that isn't the case. You can adjust how much effort you need to push back in order to recline and also lock the recline in place. Both are good features. However, I really wish it reclined a little bit further back. The lumbar support is hidden away. That's not disrupting the minimalist aesthetic. Unlike the very obvious gamer chair pillows which I have always found awkward to look at, lumbar support is very important and just because your chair claims to have one that doesn't mean that it actually does any supporting.  The tension of the MT-7 lumbar support is also adjustable through a metal dial in the back which, as I mentioned earlier, adds positively to the overall aesthetics. My biggest problem with the MT-7 is the cushioning for the armrests. After a couple of hours, my elbows definitely felt pain while gaming. My solution was to rest my arms on the tabletop itself. Now, I don't think this would be a problem if my desk and the MT-7 were ergonomically compatible. Armrests and the tables should always be leveled with each other. Something which my ergonodynamic walked me for years. My table is on the taller side and so this is more my mistake than that of Sihoo. However, if you have a similar desk problem in which your elbows carry the bulk of the support, then beware that this plastic rest is unforgiving. In general, this plastic support is definitely on the harder side and I would have preferred they went with the black padding of the latest ergonomic, a padding of which seems identical to that of my Aeron chair. So I don't think it's overall that expensive to add. The MT-7 allows Indian sitting, single leg sitting and cross leg sitting. If you listen to most ergonomic guides, however, these should never be done and you should always have your feet firmly on the ground. If you can't reach the floor, then get a footrest, which I did.  I did not think that the mesh was sharp enough to dig into my skin, but others might feel that it does. I did not use the wheels which came with the MT-7. Instead, I bought these affordable roller blade casters which protects any kind of flooring you have, whether that is wood, carpet and so forth. And it greatly improves the rolling functionality of your chair. It's essentially the equivalent of chair ice skating without the ice. If you aren't looking to invest in a new chair, please do yourself a favor and pick these up. They are less than a thousand pesos and they are compatible with pretty much any office or gaming chair. TW Today retails the specific model of the Sego M57 for 9,500 pesos. There is a slightly more expensive version of this with a leg rest. However, I find chairs with these to be strange-looking and will advise you to just go with this one instead, simply because I think nothing beats a good feet-on-table recline. While this might not be an extremely budget priced, after owning a similarly priced chair for eight whole years without a problem, a good chair pays for itself. I bought my Herman Miller Aeron for 60,000 pesos which is already a large discount from the original price of 80,000. And what I learned is that there is only so much additional comfort an expensive chair can provide for you. Yes, the Aeron is hands down the more comfortable chair. However, the comfort level isn't galaxies apart. I am very picky with chairs and if I were to rate the comfort of this M57 from 1 to 10, I'd rate it a 6.8 out of 10 while my Aeron's comfort rating is a 8.5 out of 10. In short, in terms of bang for buck and decency, I think the Sihu M57 is pretty spectacular cost-benefit-wise and I have worked from this chair for many hours a day with no problem other than that gaming armrest problem. Most of our viewers are from our native Philippines and so this video is for you guys who are interested in ordering the Sihu M57 off Shopee or Lazada. However, one very important thing most local reviewers fail to mention is that the Sihu M57 is also being sold on Amazon. Which while we don't order stuff from there, I have found that the reviews posted on Amazon is extremely helpful because I make a purchase on any product I see locally based on their reviews. One of my problems with my beloved ergonomic chair is that it seems to be sold purely by only one seller in the Philippines and other than our own video review there are no other outside reviews to depend on. Sihu, whether you hate or love their products has global reach and that's a good thing because it means there are a lot of other online reviewers you can watch other than this video to help you decide if the M57 is the chair for you. You should always be concerned if you are making a purchase on a product simply because one person told you to and that includes videos from Hardware Sugar. I say safe because Sihu is a known brand and has a lot of online reviews for you to inspect after this video. It also takes off all the essential good qualities a chair should have, which is an attractive design and good and long lasting build quality. I know I've had the chair for only three months as opposed to the ergonomic which I've had for eight years but when you've sat on enough chairs as I have you get a feel for what will last and what won't. My only big complaint is that the armrests are pretty hard. The recline doesn't go as far back as I like and the headdress might be more comfortable if I had the option to push it back further. So, which would I recommend? The Sihu M57 or the Ergo Dynamic? It's a tough call but the safe choice would be the Sihu M57 mostly because there are a lot more vendors for you to choose from and a lot of other experiences you can read about with respect to this chair. The Ergo Dynamic is sold by a supplier who was disinterested when I approached them for more details regarding the chair. In terms of overall comfort though the Ergo Dynamic wins by a few points. Again, the Ergo Dynamic reclines further back. The armrests are softer and it has literally stood the test of time of eight years. Let me know in the comment section below if there are any lingering questions you may have regarding the M57 or the Ergo Dynamic and if you want a dedicated video on specific chair comparisons. Next coming up is an Aeron chair review. Again, special thanks to TWU for sending us the M57. I'll leave a link to their shop in the video description. But again, this isn't sponsored by anyone so feel free to buy your chair from whoever you wish. Thanks and stay safe everyone. We want to give an extremely special thanks to our top fans who helped make all of our work possible. ITX addict, Rafael James, Ian Meru, Ian Magnae, Richard O'Kinko, John Rubin Otia and Christian Espinosa. It's good seeing all of you so regularly during our streams and again, thank you so much for the support.


With what we're doing here the DAV and Wells Fargo is really making a huge impact on the Bob being able to stay in this house longer So we've got a new heater going in replacing a 35 year old heater that was in the house So he's gonna see a lot of savings there and a lot of energy efficiency Got a brand new garage door and then the work going on behind me is we're doing some landscaping There was some terrible erosion and water problems And so we're giving them a whole new landscaping around this house to be in the home alone And to know that I can stay in this house for years and years and not have to worry about the hill encroaching on the house For my wooden garage door falling in And the new high-efficiency furnace It's amazing Just the money I'm gonna save on that alone will pay for itself. We have put a Goal out there of focusing one billion dollars on housing affordability and the event today is in my realm of work It's focused solely on philanthropic outreach to veteran causes So Disabled American veterans is one of the longest-standing veteran service organizations in this nation So couldn't think of any any better partnership to have we haven't worked with DAV before And so this is kind of a big deal for us and we got a good crew out here to Try and get this job done as soon as we could I'm amazed at how fast everything is going and how good it looks because Not very many of us are builders or landscapers. So it's It's looking good. We got good leadership. I love projects like this. I mean Obviously, I'm not a young person but I'll haul bricks all day long for volunteer events like this because it's meaningful to me We have limited resources as a nonprofit as a service organization And so these kind of partnerships is something we hope we can grow and continue with groups like Wells Fargo He really wasn't able to put words with it But you can tell in space the smile the fact that when we got here this morning He he ran out here to thank us. He's one of the first person to say thank you You know it means a lot to him and we're just proud and honored to be able to give this opportunity to him being disabled and Only having half my lungs. I could never have done this I mean look at the crew look at you guys you did all the work to enjoy that three-season porch in the back and watch the deer Watch the birds and that's my favorite room and you put that wall up there to protect that room and from the bottom of my heart Thank you. This is something that wouldn't have happened without the AV and the volunteers that came out today Project is big as you can see behind me. We're doing some major major landscaping It's not easy work, but everybody's grinning and everybody's excited about what they're doing and how it's changing the veterans life I have a soft spot in my heart for for veterans And just to be able to help help out veterans and do the kind of things we do is a very rewarding thing To see the partnership between the DAV DAV national DAV Minnesota Wells Fargo its MC construction It is all just a it's amazing. It really makes this happen Everybody's focused on one goal and that's helping out a veteran here today Visit DAV MN org