u/TurboCessna

I’m a Cal alum currently doing a robotics PhD at Georgia Tech, and I have a few openings for students who are interested in contributing to a machine learning and/or robot navigation research project. The work can be done remotely.

There are two main ways students could get involved.

The first is more machine-learning focused. This part of the project looks at predicting the motion of nearby agents using previously collected robot navigation data. Because most of this work is separated from the robotics software stack, you do not need prior experience with robotics or ROS. The work would mainly involve working with datasets, training models, tuning them, evaluating their performance, and testing ways to improve the predictions.

Our current model is GRU-based, but there is a lot of flexibility in what could be explored. Possible directions include LSTMs, transformers, alternative sequence models, different input representations or preprocessing methods, and other architectural changes.

There is also a robotics/navigation-focused side of the project for students who are more interested in planning than in machine learning. Possible work includes developing and evaluating trajectory cost functions, improving navigation behavior, and experimenting with changes to the local planning framework. The specific direction can vary depending on a student’s experience and interests.

For some technical context, the project builds on the Dynamic Gap navigation framework. The current planner uses a Kalman-filter-based method to estimate how gap endpoints move over time. One research direction is exploring whether a learned model can replace or supplement this estimator to improve predictions in dynamic environments. Other directions involve using these predictions to improve how candidate robot trajectories are generated and evaluated.

The lab is planning to submit a paper in the near future, potentially later this year, so there may also be opportunities for co-authorship for students who make substantial contributions.

When I was at Cal, I was able to receive research units while working on a project at another university. I’m not sure whether that option is available in every department, so you would need to check the specific rules for your program.

If interested, please email azaro3@gatech.edu with your CV and a short paragraph describing your interest in the project and how your background aligns with the work.

I’m a robotics PhD student at Georgia Tech looking for students who may be interested in joining a research project involving machine learning and autonomous robot navigation this fall.

There are a few different ways to contribute depending on your background.

One direction is primarily machine-learning focused and does not require previous robotics or ROS experience. The goal is to use navigation data to better predict how nearby agents will move over time. Students working on this side of the project would mainly work with datasets and learning models: training models, tuning hyperparameters, evaluating performance, experimenting with different inputs, and potentially implementing improved approaches.

We currently have a GRU-based model, but the specific architecture is not fixed. There is plenty of room to explore alternatives such as LSTMs, transformers, different sequence-modeling architectures, feature/input preprocessing, or other ideas that might improve prediction performance.

A second direction is more robotics/navigation focused. This could involve improving how the local planner generates or selects trajectories, designing new trajectory cost functions, evaluating navigation behavior, and testing modifications to the planning framework. The exact work can be adapted somewhat based on a student’s experience and interests.

For some additional technical context, the project builds on the Dynamic Gap navigation framework. The current system uses a Kalman-filter-based method to estimate the motion of gap endpoints in dynamic environments. One research direction is investigating whether learned temporal models can replace or supplement this estimator. Another is studying how improved motion predictions can be incorporated into trajectory generation and evaluation so that the robot makes better navigation decisions around moving agents.

Students can potentially participate through graded or ungraded research credit, likely in the range of 1–5 credits. There is no guaranteed funding available at the moment, although students who make strong progress could potentially be considered for paid work later. Remote or hybrid participation may also be possible depending on the project and circumstances.

The lab is also planning a future paper submission, so students who make substantial research contributions may have an opportunity for co-authorship.

If you’re interested, email azaro3@gatech.edu with your CV and a short paragraph explaining your interest in the project and how your background might fit.

Our robotics lab is recruiting students for a fall research project focused on robot navigation and planner evaluation using Arena-Rosnav 5, an open-source ROS2 simulation framework for benchmarking navigation algorithms in dynamic environments.

Unlike a project where the main task is implementing a new planner from scratch, this role focuses on working with navigation planners that are already implemented and running in Arena-Rosnav. The student would learn how one or more planners work, test them systematically across different environments and scenarios, and analyze their strengths, weaknesses, and failure modes.

A major part of the project is understanding why different planners behave the way they do. This could involve analyzing collisions and navigation failures, identifying environmental conditions where a planner struggles or performs particularly well, studying how those behaviors relate to the assumptions or design of the planner, and comparing different navigation approaches experimentally.

The work will involve directly using the Arena-Rosnav 5 / ROS2 navigation stack, including running and configuring experiments, working with simulation environments, examining navigation behavior, and collecting and analyzing experimental results. Depending on progress and interest, there may also be opportunities to work on other parts of Arena-Rosnav, such as debugging planner or simulation behavior, improving experimental or data-logging infrastructure, modifying scenarios, or investigating implementation-level issues.

Prior ROS experience is strongly preferred. Students without ROS experience may still be considered if they have substantial hands-on robotics experience and are comfortable learning a large robotics software stack. Experience with ROS2, Linux, C++, Python, robot navigation, or motion planning would all be useful. Prior research experience is not required.

Students may take graded or ungraded research credits (likely 1–5 credits). The project does not currently have guaranteed funding, although students who make strong progress may later be considered for paid project work. Flexible remote or hybrid arrangements may also be possible depending on the student and the tasks. There may also be opportunities for co-authorship if a student makes a substantial contribution to research resulting from the project.

If interested, please email your CV along with a short paragraph describing your robotics/ROS background and why you are interested in the project to azaro3@gatech.edu.

I’m a Cal alum and current robotics PhD student at Georgia Tech. I have a few spots for students interested in helping with a machine learning and/or robot navigation research project. The work can be done remotely.

One major direction focuses on using learning-based methods to predict the motion of nearby agents from collected navigation data. This work is mostly independent from the robotics pipeline, so no prior robotics or ROS experience is required. Students interested in this direction would primarily work with datasets and focus on training, tuning, evaluating, and improving learning models.

The current approach uses a GRU-based model, but there is significant flexibility for exploration and improvement. Possible directions include LSTMs, transformers, input preprocessing, alternative architectures, or other sequence modeling approaches.

There are also opportunities for students who are more interested in robotics and navigation than machine learning. Potential work includes designing and evaluating new cost functions for trajectory selection, improving navigation behaviors, and exploring other modifications to the local planning framework. There is flexibility in the specific direction depending on a student’s background and interests.

Additional project context: the Dynamic Gap navigation framework currently estimates the motion of gap endpoints using a Kalman-filter-based approach. One direction of this project explores replacing or augmenting that estimator with a learning-based component to improve prediction in dynamic environments. Other directions focus on improving how the planner generates and evaluates candidate trajectories using these predictions.

The lab is also planning to submit a paper in the near future, potentially later this year, and there may be opportunities for co-authorship. When I was at Cal I was able to take research units while working on a project at another university, but I am unsure whether you will be able to depending on your department.

If interested, please email your CV along with a short paragraph describing your interest and alignment with the project to azaro3@gatech.edu.

I’m a robotics PhD student looking for students interested in helping with a machine learning and robot navigation research project starting this fall.

One major direction focuses on using learning-based methods to predict the motion of nearby agents from collected navigation data. This work is mostly independent from the robotics pipeline, so no prior robotics or ROS experience is required. Students interested in this direction would primarily work with datasets and focus on training, tuning, evaluating, improving learning models and maybe implementing them.

The current approach uses a GRU-based model, but there is significant flexibility for exploration and improvement. Possible directions include LSTMs, transformers, input preprocessing, alternative architectures, or other sequence modeling approaches.

There are also opportunities for students who are more interested in robotics and navigation than machine learning. Potential work includes designing and evaluating new cost functions for trajectory selection, improving navigation behaviors, and exploring other modifications to the local planning framework. There is flexibility in the specific direction depending on a student’s background and interests.

Additional project context: the Dynamic Gap navigation framework currently estimates the motion of gap endpoints using a Kalman-filter-based approach. One direction of this project explores replacing or augmenting that estimator with a learning-based component to improve prediction in dynamic environments. Other directions focus on improving how the planner generates and evaluates candidate trajectories using these predictions.

Students may take graded or ungraded research credits (likely 1–5 credits). The project does not currently have guaranteed funding, but students who make strong progress may later be considered for paid project work. Flexible remote or hybrid arrangements may also be possible depending on the situation. The lab is also planning to submit a paper in the future and there may be opportunities for co-authorship.

If interested, please email your CV along with a short paragraph describing your interest and alignment with the project to azaro3@gatech.edu.

I'm thinking about checking out this drift car. It's street legal and registered. He's selling it for $9,000. It has a good amount of mods, apparently about $20,000 worth. I asked the seller if he would be okay with me getting a pre-purchase inspection or something like that to have some shop look it over. Basically so I can make sure that the engine is not terrible and the frame is not damaged. But he refused and says he doesn't see the need to take it to an inspection because I can take a look at everything myself. I asked him if I can do a compression test. He said no unless I'm willing to pay for a tune-up while I'm there. And he doesn't want a shop to do a compression test on it either. I'm getting mixed flags about this car because it looks like some quality parts were installed on it, but I don't know why he would be against having a shop look at it.

I have a 330i automatic with about 200k miles. I bought it cheap, around $2000, and I’ve been slowly turning it into a drift/track car.

It was basically stock when I got it, so it was not already beaten on as a drift car. It currently has BC coilovers, a welded diff, and a refreshed cooling system. It also has new engine mounts and tie rods. I’ve taken it to a couple drift events and driven it home from track days without major issues. As far as I know, it does not have rear subframe cracking.

The main thing holding it back is that it’s automatic. I’m trying to decide if it makes sense to manual swap it, or if I should stop putting money into a 200k-mile chassis and start with something else.

Estimated costs:

• Manual swap kit: around $1000
• Labor: around $600 if family does it
• Clutch/flywheel/trans maintenance parts: $1000
• Power steering rack/hoses and other steering refresh stuff, maybe ebay angle kit: maybe around $800
• Other small drift prep / maintenance / subframe reinforcement,  bushings: $1000

Realistically, I think I’d be about $6k–$6.5k into the car by the time it is a reasonably sorted manual drift car.

The argument for keeping it is that I know this car. The engine seems good, the cooling system is done, it has not been abused its whole life, and I already own it. I also like that I can fix/upgrade things one at a time instead of buying someone else’s mystery drift car.

The argument against it is obvious: it’s still a 200k-mile E46 chassis. Even if the manual swap goes well, I’m still putting another few thousand dollars into an older car that may not be worth much when I’m done. I just want a reliable track/drift car.

Would you manual swap and keep building this car, or sell it and start with something else?

Is a 200k-mile 330i chassis worth putting this much money into if its just for drifting? 

For comparison, the other option I’m considering is a built 350Z drift car that I could maybe get for $6k–$7k.

It already has:

• VQ35DE / 6MT
• Full cage
• Seats and harnesses
• Hydro brake
• BC coilovers
• GKTech angle kit
• Front/rear arms
• Solid subframe and diff bushings
• 370Z axles and hubs
• Z1 clutch/flywheel
• Clean title, non-op

The 350Z is appealing because it is already caged and basically ready for track use, especially if I eventually want to do tandems. But it also has drift history. Seller says he bought it stock and has done around 25 events over 8 years, with only 4 events in the last 2 years.

Concerns with the 350Z:

• Original engine blew because the seller didn’t check oil and seized it while drifting
• Current engine is supposedly from a 60k-mile rear-crashed G35
• Seller says it runs well, no overheating, no knocks, no smoke
• But he suspects it burns some oil, maybe less than half a quart per drift event
• He runs 15W-40 and changes oil/filter before every event
• Oil sometimes smells a little burnt
• Car was crashed into another car Passenger door/front fender were crunched, front knuckle broke, brake line got pinched
• Seller says alignment is straight and it drives well
• Rear knuckle bushings are worn, but he has new GKTech spherical bushings and install tool

So I’m basically deciding between:

1. Keep my known E46 and manual swap it, ending up around $6 -6.5k total into a 200k-mile chassis
2. Spend $6k–$7k on a much more built/caged 350Z, but accept the risk of buying someone else’s drift car

I’m trying to decide whether it makes more sense to buy an already-built drift car or keep building my current E46.

Current car: 200k-mile automatic E46 330i. I bought it for around $2,000. It already has BC coilovers and a new cooling system. The engine seems healthy and the car was stock when I bought it, so I at least know it probably wasn’t abused as a drift car before me.

To make it drift-ready, I’d need to manual swap it and do the usual drift prep/maintenance. The manual swap would cost me about $2,500 total. By the time I do bushings, reinforcements, steering/rack stuff, and other drift-related repairs and maybe ebay angle kit, I’m guessing the entire car would have costed me around 5500.

The other option is an already-built 350Z drift car for 6-7k. 6MT VQ35DE with a cage, seats/harnesses, BC coilovers, GKTech angle kit, hydro/dual rear calipers, solid bushings/mounts, Z1 clutch/flywheel, welded 370Z diff, and 370Z axles/hubs.

The Z looks well-maintained from the listing and seems like a good amount of time and money went into it. The seller says it will pass tech, alignment is straight, and it drives well. Clean title, but currently registered non-op.

My main concern is wear. The seller says the car has around 25 drift events total, and the newer engine probably has around 10 events on it. The first engine apparently blew because it was run low on oil, but the seller says he has been more careful since then.

Is 25 events a reasonable amount for a drift car, or is that already getting into “beat to death” territory?

One thing I’m wondering is whether buying a car that has already survived drift events is actually an advantage because a lot of weak parts have already been replaced, or whether it’s the opposite and I’d just be buying someone else’s problems.

I’d like to do tandems in a year or two, so the cage is a big plus. I’m not worried about cosmetics. This would be a track car.

What would you guys do?

Option 1: build my 200k-mile automatic E46 into a drift car
Option 2: buy the already-built/caged 350Z that has seen drift events

I’m mostly trying to figure out which option is less likely to turn into a money pit and which one makes more sense long-term.

I’m a PhD student looking for students interested in helping with a machine learning project related to robot navigation in my lab.

The project focuses on using learning-based methods to predict the motion of nearby people from collected navigation data. The work is mostly independent from the robotics pipeline, so no prior robotics or ROS experience is required. Students will primarily work with datasets and focus on training, tuning, evaluating, and improving learning models.

The current approach uses a GRU-based model, but there is significant flexibility for exploration and improvement. Possible directions include LSTMs, transformers, input preprocessing, alternative architectures, or other sequence modeling approaches.

Additional project context: the navigation framework currently estimates the motion of gap endpoints using a Kalman-filter-based approach. This project explores replacing or augmenting that estimator with a learning-based component to improve prediction in dynamic environments.

Students may take graded or ungraded research credits (likely 1–5 credits). The project does not currently have funding, but students who make strong progress may later be considered for paid project work in the fall. Flexible remote or hybrid arrangements are possible depending on the situation. I'm planning to submit a paper in the near future (potentially later this year), and there’s an opportunity for co-authorship.

If interested, please email your CV along with a short paragraph describing your interest and alignment with the project to azaro3@gatech.edu.