I’m working on a mini self-driving car project with Ackermann steering. The car has to follow a lane with boundary lines on both sides, stay centered, and handle both straight and curved sections like a small F1-style track. I plan to use OpenCV + a webcam for lane detection and then control the steering based on the detected lane center. I’m looking for ideas on lane detection methods, steering control, and how to make it robust on curves and under different lighting conditions submitted by /u/Risheyyy [link] [Kommentare]
My background is a programmer. Recently I decided to build a robot for chatting and daily use. For chatting, it's easy to implement by connecting it into a LLM. But that's all about software stuff, you can just implement it on a phone. So I want to make it movable. But I don't know what can I do in daily life for its mobility. Sorry for my poor imagination, all I can think out is that I can make it a new born duck, so it can always follow me when I walk around. So what kind of robots are you building by yourselves? Can anyone help me to get some cool ideas about my robot? My budget is about 500~1000 dollars. Feel free to comment or send a DM to me. Thanks! submitted by /u/cocoyoung [link] [Kommentare]
Background: I confounded a startup last year on Execution as a Service model. We're two confounders, and a core team of 5 guys. 4 of us used to be at xAI human data. And collectively we've worked for most of the leading genAI companies in the human data space. We started off as a managed outsourcing platform where we assign a frac COO to handle your entire outsourcing ops across functionalities which also included AI annotation and labelling. The problem: We were trying to secure contracts all over the place. Though we had 150+ registered fulfilment partners, and we secured some sizable contracts, I was genuinely confused about the growth and the direction of the company, specially with the kind of developments happening in the ops domain. I just brokered a deal valued at over 100k just for sharing internal ops data for AI training. We can't predict exactly how would the space look like. The present: The outsourcing business isn't fully justified to the kind of profiles the core team has. We were being reduced a software and marketing firm. We figured out that we need to stay relevant in the data industry. With the logistical edge that I have, and the trial run I did, I am very confident about working on physical data. We collected over 10 hours sample dataset spanning across household, industrial, construction, and electrical egocentric data. The question: Before we jump into physical data, I am genuinely looking for researchers' perspectives on ego-exocentric vs synthetic data. I understand that the upfront cost is high for synthetic, but long term cost is significantly cheaper, but how does the difference play out in the actual training workplace. TIA submitted by /u/Low_Can_4600 [link] [Kommentare]
If you've tried to get into robotics, you've probably hit one of these walls: How do I get started? How do I get from blinking an LED to an autonomous robot? I come from a software (or mechanical, or data) background, what am I missing? I couldn't find a single good answer to these, so I started building one: an open source "tech tree" for robotics. It's a visual skill map. You start at the root and unlock the rest as you go (electronics, mechanics, programming, data science, AI), with hands-on builds as the milestones: blink an LED, a sensor project, a robot arm, a robot dog, and up into more serious AI. The main idea: it's not new content. There is already a ton of great tutorials, courses, and docs out there. The tech tree is just the map that sits on top of it and points you to the right resource for each skill, in an order that makes sense. It's early and nowhere near complete, which is kind of the point. It's fully open on GitHub, so if you have a favorite tutorial, a course that made something finally click, or a resource you wish you'd found sooner, you can add it. PRs and issues welcome, and "you forgot X" comments even more so. Links: Website: https://www.backtoengineering.com/ Repo: https://github.com/iuliaferoli/backtoengineering What would you add, or what's missing from the paths? submitted by /u/Growth-Sea [link] [Kommentare]
Fruit harvesting remains one of the most challenging robotics applications. Detecting ripe fruit is becoming easier with modern vision models, but reliable picking in unstructured environments is still difficult. Do you think the biggest challenge today is: • Vision? • Motion planning? • End-effector design? • Cost? Curious to hear different opinions. submitted by /u/ROS_RobotAlisa [link] [Kommentare]
Created Simulation From LLM Output Background: I studied EECS and I'm now getting into robotics, mostly working through the simulation side of things. While digging into the sim pipeline I came across this repo: https://github.com/AlexKaravaev/world-creator It's a CLI that generates Gazebo and Mujoco simulation worlds from a text prompt. You type something like "warehouse with shelves and some obstacles for navigation testing" and it picks models from the Gazebo model database and places them for you. I think it's genuinely a great idea and ahead of its time. It's from ~2023, so it predates all the recent LLM progress, and the author was upfront that the model hallucinated a lot back then. With today's models this approach could work way better. Curious about a few things: Has anyone here used this or something like it in real work? Is prompt-to-world something you'd actually want, or is scene setup not painful enough to matter? From what I've seen so far, people complain way more about getting the robot itself into sim (URDF, meshes, inertia values) than the environment around it. Is that right? If someone built an upgraded version of this, what would the use cases be for you? Randomized scenes for RL training? Test scenarios in CI? Quick demos? I'm exploring building in this space, so honest "nobody needs this" takes are just as useful as feature wishlists. submitted by /u/Few_Film8907 [link] [Kommentare]
Teaching my 13-year-old grandson programming using Arduino, Python, and AI. We are currently programming this small robotic arm. I originally built the arm for him 5 years ago for Christmas. Back then he just played with it, but now he is writing new code for it. The goal is to detect candies placed in front of it and drop them into a cup. How it works: A Raspberry Pi-based USB camera monitors the workspace. A Python script running on a PC detects the candies and sends G-code commands to control the arm. Hardware & Firmware: The robotic arm is powered by an STM32F103 microcontroller running Arduino-based firmware. submitted by /u/Available-String-891 [link] [Kommentare]
I saw other products that can do this, so I also want to use my dtof lidar HM-LD1 to implement this. I haven't finished yet, just share a video first. I believe I am about to realize my idea. Looking forward to it, I will open source it on GitHub. submitted by /u/RiskHot1017 [link] [Kommentare]
Hello, my name is Noah , I’m 14 years old and I’ve been building this humanoid robot from scratch. I designed the parts in CAD, 3D printed them, assembled the servos, and now I’m working on the software and walking algorithms/ gaits . It’s powered by a Raspberry Pi and uses multiple servo motors for its joints. There’s still plenty to improve, but seeing it come together has been really rewarding. I have posted it on TikTok and Instagram under the name of NoahisRobotix , but have not been that successful so far.. I’d love to hear what you think or answer any questions! submitted by /u/Equivalent_Ask_1156 [link] [Kommentare]
First real drive outside. Before this it only ran on the bench. Build: four 10.5" hoverboard hub motors, 2x ODrive (ODESC 3.6), Raspberry Pi 5, aluminium profile frame. Control is fully manual right now, from a laptop over 4G: live camera, per-motor telemetry, ARM / E-STOP. Main thing I learned: skid-steer turning depends heavily on surface grip, so that has to go into the control logic rather than being tuned by feel. Next step is autonomous A-to-B: 3D lidar, ODrive on CAN with closed-loop wheel control, ROS2/Nav2 for planning. Still early. Happy to answer anything about the build. submitted by /u/NiamorroMilky [link] [Kommentare]
Hey everyone, I've been building CARL, an embodied virtual primate scout in MuJoCo. He has a 28-DOF body, 3-fingered hands, and is controlled by a multi-tiered biologically-inspired cognitive architecture. The codebase is fully open-source, but it is far from perfect. We are looking for help, critiques, and design upgrades on absolutely everything: 🛠️ 1. Physical Design & Body Upgrades (Morphology/Hardware) - Suggestions for better hand/gripper layouts (should we go to 5-fingers or use compliant pads?). - Better limb length proportions to maximize reach and avoid joint locking. - Optimal positioning of LiDAR, camera sensors, and tactile grids. - Bipedal chassis balancing and layout suggestions. 🎮 2. Control Loops & Trajectories (Robotics/Controls) - Smother arm trajectory models to fix our sudden joint-angle jumps and snapping. - Calibrating physics contact constraints in MuJoCo to stop fingertip clipping. - Alternatives to our current Damped Least Squares IK solver. 🧠 3. Cognitive Architecture & AI Models (Deep Learning/RL) - Optimizing our PPO actor-critic network and Liquid Time-Constant (LTC) arm policy. - Better reward shaping to speed up learning convergence (currently too slow!). - Improving how our high-level emotional drives (dopamine, cortisol) link down to motor execution. We are completely open to any feedback, design overhauls, or code contributions. Take a look at the repo and tell us what you would change! * GitHub Link: https://github.com/Manassadashiv/carl-simulation * Honest breakdown of our struggles: https://github.com/Manassadashiv/carl-simulation/blob/main/CONTRIBUTING.md submitted by /u/Manas_Sadashiv [link] [Kommentare]
I've been working on tlabel, an open-source Python toolkit that loads tactile sensor data (GelSight, DIGIT, PaXini, etc.) into a unified format. One thing we struggled with: how to define and label manipulation primitives consistently. The T-Rex paper (Tactile-Reactive Dexterous Manipulation) defined 22 motor primitives for dexterous manipulation — grasp, press, wipe, twist, poke, and so on. That's probably the most comprehensive taxonomy out there right now. But not every task needs all 22, and not every lab uses the same definitions. So we built a configurable taxonomy system on top of tlabel, with T-Rex's set as the default starting point. How it works We picked 7 primitives from T-Rex that have clear force signatures (reach, grasp, press, squeeze, wrap, wipe, lift), plus Cutkosky grasp subtypes. The engine can auto-predict these from visual-tactile images — even without a force sensor, it estimates force distributions from GelSight/DIGIT images and maps patterns to primitives. python import tlabel data = tlabel.demo('gelsight') data.predict_primitives() Every prediction carries a source tag (ai_predicted vs ai_predicted_estimated vs manual) and a confidence score. Low-confidence segments are left blank for you to annotate. Defining your own primitives If your task has primitives not in the default set, you can register custom ones with physical rules: python tlabel.register_custom_primitive('poke', force_range=(0.1, 0.8), deformation_max=0.15, contact_required=True, confidence=0.5 ) data.predict_primitives(min_confidence=0.4) Or scope it to a local taxonomy without polluting the global registry: python taxonomy = tlabel.get_default_taxonomy() from tlabel import PrimitiveRule taxonomy.register(PrimitiveRule( name='poke', min_force=0.1, max_deformation=0.15, contact_required=True, min_confidence=0.5 )) data.predict_primitives(taxonomy=taxonomy, min_confidence=0.4) Manual annotation still works python data.add_primitive('reach', start_frame=0, end_frame=10) data.add_primitive('grasp', start_frame=10, end_frame=25) data.add_primitive('lift', start_frame=25, end_frame=40) data.get_primitive_timeline() # [('reach', 0, 10), ('grasp', 10, 25), ('lift', 25, 40)] Export python data.export("output.csv") # Columns: primitive_label, primitive_source, primitive_confidence The design principle is "assist, not autoritate" — AI predictions are suggestions with metadata, not ground truth. You stay in control. Pure Python, MIT license, no dependencies beyond numpy. Code: https://github.com/liesliy/tlabel Curious what primitive sets other people are using for their manipulation tasks. submitted by /u/ImmediateArm7942 [link] [Kommentare]
I see a lot of smaller parts are costlier than the usual sizes. Even for screws , it sometimes costs 2k rs. Why is this? Don't tell that it's because of the import duty. submitted by /u/MotorPsychology1712 [link] [Kommentare]