标签：#mpc

Model Predictive Control for Real-Time Robot Navigation A path planner tells a robot where it should go. A controller determines how the robot should move to follow that path. Model Predictive Control (MPC) repeatedly predicts future behavior and chooses control inputs that optimize a short horizon. MPC Concept Current State | v Predict future states | v Optimize control sequence | v Apply first control | v Measure new state | +----> Repeat The key idea is that the entire control sequence is not executed at once. Only the first action is applied before the problem is solved again. Robot Model For a simple differential-drive robot: x_dot = v cos(theta) y_dot = v sin(theta) theta_dot = omega The controller can predict where the robot will be after applying candidate velocity commands. Optimization Objective A typical objective might penalize: Distance from reference path Heading error Excessive control effort Rapid control changes Collision proximity Conceptually: Cost = tracking_error + control_effort + smoothness_penalty + obstacle_penalty Prediction Horizon Suppose the controller predicts: t0 -> t1 -> t2 -> t3 -> t4 For each candidate control sequence it estimates the resulting trajectory. The optimizer selects the best feasible sequence. Obstacle Handling A cost function can strongly penalize trajectories near obstacles: Obstacle ### ##### ### \ predicted trajectories \---- safe \--- unsafe Hard constraints can also be used when collision avoidance must be guaranteed by the optimization formulation. ROS 2 Architecture /global_plan | v /mpc ^ | /odom /imu /local_costmap | v /cmd_vel Real-Time Requirements MPC is computationally heavier than simple feedback controllers. Monitor: Optimization time Control frequency Solver failures CPU utilization Prediction horizon Sensor latency If optimization misses its deadline, the system needs a safe fallback. Practical Implementation Strategy Start simple: Define a robot model. Implement trajectory prediction. Define tracking

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NVIDIA shipped a 30-billion-parameter model that can see, hear, and talk — and gave the weights away. The catch: the seeing and hearing parts didn’t run on a Mac. So I spent an afternoon writing the missing piece. Here is the whole thing in thirty-five seconds — the model reading a real cart off my own store, on the laptop, with nothing leaving it. Thirty-five seconds: the model reads a real cart off my own store, on the laptop, with nothing leaving it. The elapsed counter is the real measured latency. Every couple of weeks I go looking for whatever new open-weight model just dropped, pull it onto my laptop, and see what it can actually do. Most of the time it’s a coding model, I run it against the one I already use, and my current favorite wins again. That’s a fine result. It’s just not much of a story. This time I found something different. What Nemotron Omni actually is NVIDIA released Nemotron-3-Nano-Omni-30B-A3B — a “tri-modal” model, which is a fancy way of saying one brain with eyes and ears attached. You can hand it a picture, a sound file, or a video, and talk to it about what it saw or heard. It’s 30 billion parameters total, but only about 3 billion of them fire for any given word, which is why something this capable can run on a laptop at all. The weights are public. Someone had already done the hard, unglamorous work of shrinking it down to a 4-bit MLX version that fits on Apple Silicon — that’s yayr over at mlx-community, and this project doesn’t exist without that upload. About 19 GB on disk. Ready to go. Except for one line, buried in the model card: The text backbone loads with standard MLX nemotron_h tooling. The vision and audio towers require a multimodal runtime that implements the C-RADIO ViT-H and Parakeet Conformer forward passes (e.g. the Evorix on-device engine). Translated: the brain works on a Mac. The eyes and ears don’t. The weights for them are right there in the file — all the knowledge, sitting on your disk — but nothing I could find