Advancing Beyond MVP Constraints in Robotics

I am delighted to share an interesting example of stabilizing a Car-like mobile robot (CMR) using a Nonlinear Model predictive controller (NMPC) optimal controller, to avoid obstacles and overcome barrier limitations. This is achieved by integrating the Artificial Potential Field (APF) method, with extended Kalman Filter (EKF) to estimate longitudinal and lateral position and drive the mobile robot to track a given trajectory while adhering to environmental constraints. CMR is typically modeled using its kinematic equations, capturing its nonholonomic constraints and motion characteristics.this often involves a bicycle model, where the robot is simplified to two wheels, a steerable front wheel and a fixed rear wheel. The state variables usually include the robot's position, orientation, and steering angle, while the control inputs are the linear velocity and Ang velocity.This model is essential for designing controllers. NMPC works by repeatedly solving an optimization problem over a finite prediction horizon at each control step. For a car-like robot, this involves using a dynamic model of the robot to predict its future states, such as position, orientation, and velocity, based on the current state and a sequence of control inputs. 'fmincon' solver in MATLAB solve this constrained nonlinear optimization, it aims to minimize a cost function that typically includes terms for trajectory tracking error, control effort, and adherence to constraints like obstacle avoidance, actuator limits, or road boundaries. By solving this problem in real-time, NMPC generates optimal control actions that drive the robot toward its goal while respecting system constraints and adapting to changes in the environment, if the robot detects an obstacle, NMPC can replan its trajectory to avoid collisions while still progressing toward the target. The integration of APF ensures smooth obstacle avoidance, while EKF provides accurate state estimation for robust control. This combination makes NMPC highly effective for CMRs operating in dynamic or uncertain environments, ensuring safe, efficient, and precise navigation. Overall, this approach showcases a powerful framework for autonomous navigation and control of CMR. In the future, the proposed framework for stabilizing a CMR can be further enhanced by exploring alternative techniques and solvers. For instance, Reinforcement Learning or Deep Learning could be incorporated to improve obstacle avoidance and trajectory planning in highly dynamic environments, enabling the robot to learn from experience and adapt to complex scenarios. solvers like IPOPT or CasADi could be tested alongside `fmincon` to improve computational efficiency and scalability, especially for large-scale problems. These advancements would not only improve the performance and robustness of the CMR but also expand its applicability to more challenging environments, such as urban autonomous driving or multi-robot coordination.

A recurring lesson from evaluating and backing autonomous systems is that real-world performance is often limited by factors other than model capability alone. Advances in AI models, including more capable LLMs, have significantly expanded what systems can reason about and express, yet in practice the bottlenecks are frequently elsewhere in the autonomy stack. As autonomy increases, control under uncertainty tends to become one of the top challenges, even as better models expand the envelope. Real-world environments, digital and physical alike, are partially observable, noisy, latency-constrained, and non-stationary. Systems often need to act before uncertainty is resolved, and those actions have real consequences. Waiting for certainty can be its own failure mode. At lower levels of autonomy, uncertainty is absorbed by human oversight or by tightly constrained action spaces. As systems take on more responsibility, they must manage uncertainty internally by maintaining a working view of the world, committing to actions, and recovering when outcomes diverge from expectations. This is why predicting autonomous behavior in real-world settings can be challenging. Accuracy on isolated tasks says little about how a system behaves over time, how small errors compound, or how effectively it detects and corrects its own failures. Typically, the hardest cases are long-horizon tasks where feedback is delayed and mistakes accumulate quietly. Early on, autonomy can appear easier than it is because initial deployments deliberately limit these challenges. Expanding autonomy responsibly requires demonstrating control under progressively harder conditions. Ultimately, autonomy is less about reaching a finish line and more about extending the range of conditions under which a system can be trusted to act.

1. Scan 2. Demo 3. Track 4. Render 5. Train models 6. Deploy What if robots could learn new tasks from just a smartphone scan and a single human demonstration, without needing physical robots or complex simulations? [⚡Join 2400+ Robotics enthusiasts - https://lnkd.in/dYxB9iCh] A paper by Justin Yu, Letian (Max) Fu, Huang Huang, Karim El-Refai, Rares Andrei Ambrus, Richard Cheng, Muhammad Zubair Irshad, and Ken Goldberg from the University of California, Berkeley and Toyota Research Institute Introduces a scalable approach for generating robot training data without dynamics simulation or robot hardware. "Real2Render2Real: Scaling Robot Data Without Dynamics Simulation or Robot Hardware" • Utilises a smartphone-captured object scan and a single human demonstration video as inputs • Reconstructs detailed 3D object geometry and tracks 6-DoF object motion using 3D Gaussian Splatting • Synthesises thousands of high-fidelity, robot-agnostic demonstrations through photorealistic rendering and inverse kinematics • Generates data compatible with vision-language-action models and imitation learning policies • Demonstrates that models trained on this data can match the performance of those trained on 150 human teleoperation demonstrations • Achieves a 27× increase in data generation throughput compared to traditional methods This approach enables scalable robot learning by decoupling data generation from physical robot constraints. It opens avenues for democratising robot training data collection, allowing broader participation using accessible tools. If robots can be trained effectively without physical hardware or simulations, how will this transform the future of robotics? Paper: https://lnkd.in/emjzKAyW Project Page: https://lnkd.in/evV6UkxF #RobotLearning #DataGeneration #ImitationLearning #RoboticsResearch #ICRA2025

Lately I’ve been hearing the same conclusion from multiple robotics founders who are deploying real systems at scale (not demos): Robotics needs a purpose-built Intelligence Processing Unit (IPU). Today, most advanced robots run on general-purpose AI hardware from NVIDIA (Jetson Orin / Thor). It works - but it’s increasingly clear that it’s not designed for what robotics actually needs.... Here’s what founders are running into: • Cost doesn’t scale   $5k–$6.5k per robot compute box can kill large deployments. At hundreds of robots, hardware alone becomes the bottleneck. • AI ≠ robotics compute   Robots don’t just need inference. They need four things at once:   AI inference, real-time deterministic control, functional safety, and industrial I/O. Today’s stacks handle the first well and bolt the rest on awkwardly. • Safety breaks intelligence   In industrial environments, safety PLCs often override AI decisions. The result: half the intelligence goes unused, throughput drops, and downtime increases. • Determinism matters more than FLOPS   Sub-microsecond jitter, guaranteed timing, and certified execution paths matter more than raw GPU flexibility. • Ecosystem lock-in slows scale   Training, inference, tooling, and deployment are all tied to one vendor’s stack. That’s expensive and limits experimentation. What many founders are converging on: A robotics-native IPU that combines: – Vision + neural compute   – Deterministic real-time control   – Safety-aware execution   – Industrial I/O   – Shared memory   – Open or editable tooling  At a price point closer to $500, not $5,000. If that layer exists, companies can compete on models, systems, and distribution - instead of fighting hardware constraints. Curious to see where this goes 🙌