Real-Time Control Strategies for Robotic Systems

A key challenge in using VLAs and other imitation learning models for control is ensuring **temporally consistent actions**. To address this and compensate for slower inference rates, models often predict an entire action trajectory—an "action chunk"—instead of a single action. For real-time control, this chunk is executed in parallel with ongoing policy inference allowing the model to "think" about future actions in parallel to executing the previous ones. With the shift to action chunking, a key challenge becomes ensuring that consecutive chunks remain consistent with previously executed ones. A common but naive approach has been to average actions over past chunk also known as **temporal ensembling**. However, this adds delay on top of inference latency, leading to several problems: ❌ Potentially Infeasible actions when averaging over mode switches. ❌ Reduces the reaction time to new information which can lead to failure in real-time critical behaviors. ❌ Hard to tune for both smooth and reactive behaviors ❌ Tends to over-smooth actions leading to slow policies. The main idea in **Real-Time Chunking (RTC)** we developed at Physical Intelligence is to treat generating a new action chunk as a diffusion style inpainting problem, where the actions that will be executed while the robot “thinks” are treated as fixed, while new actions are inferred via flow matching. Since this is only an inference side change any diffusion or flow policy can be adapted to use RTC without the need for training-time changes. We evaluated the the proposed method on our π0 and π0.5 models observing the following benefits: ✅ Makes the robot move with higher precision and speed ✅ Results in smoother robot motions ✅ Improved policy performance and robustness to model inference delays Detailed results can be found in our paper: https://lnkd.in/eMKTeMuM

End-to-end motion planning simulation built on the CARLA simulator that seamlessly integrates advanced path planning, smooth trajectory generation, and real-time vehicle control 🚗 🔑 Key Features: - Path Planning with RRT*: I implemented sampling-based algorithms (RRT, RRT*, and Informed RRT*) to compute collision-free paths in a simulated urban environment. By dynamically sampling the CARLA world and avoiding obstacles, the planner finds viable routes even in challenging scenarios. - Smooth Trajectory Generation: Using motion primitives based on cubic polynomial interpolation, the system generates smooth trajectories between waypoints. This ensures that the vehicle’s motion is both safe and comfortable. - PID Control for Real-Time Vehicle Guidance: A combination of longitudinal and lateral PID controllers has been designed to accurately follow the planned trajectory. The vehicle’s throttle, brake, and steering commands are continuously updated in real-time. Repository: https://lnkd.in/d7qMqD-p 📖 Learn More: CARLA Open-source simulator for autonomous driving research: https://carla.org/ Robotic Path Planning RRT and RRT*: https://lnkd.in/dTqpGfJt The PID Controller & Theory Explained: https://lnkd.in/dMHYQdJB #autonomousdriving #CARLA #PathPlanning #PIDControl #Simulation #Robotics #RRT #RRT*

From Foundation to Reflex: The New Standard for Next-Gen, Scalable, and Assured Autonomy For more than a decade, autonomous vehicle development has been dominated by two opposing design philosophies: modular pipelines and end-to-end learning. Modular architectures, which break autonomy into separate perception, planning, and control blocks, offer explainability and task-specific optimization. However, they often suffer from integration complexity, brittle hand-offs between modules, and challenges in generalizing across domains. In contrast, pure end-to-end models simplify the system, directly mapping sensor inputs to driving commands. But such systems lack semantic understanding and are often unable to explain or adapt their behavior in unstructured or novel scenarios. A new architectural paradigm is emerging that aims to combine the strengths of both approaches. This involves integrating global scene understanding models with reflexive control layers. At its core, the architecture leverages semantic understanding of the environment, extracted via large-scale pre-trained models capable of processing multimodal inputs such as images, LiDAR, and radar. These models generalize across environments and provide high-level interpretations like lane topology, object classification, and actor behaviors. Crucially, this semantic representation becomes the structured input for control layers responsible for direct action generation. In essence, autonomous systems begin to drive based on what they understand, not just what they sense. Equally critical is the control side of the architecture. Reflexive control networks designed for low-latency, real-time actuation process environmental context and generate precise motion commands. These networks are optimized for direct control objectives such as collision avoidance, energy-efficient routing, and path stability. By decoupling scene interpretation from control logic, this approach allows perception and control components to evolve independently, while their fusion ensures coordinated, real-time decision-making. This structure also supports adaptive behaviors in complex, partially structured environments like logistics yards, ports, and industrial campuses, where fixed HD maps or conventional rule-based planners are inadequate. This architectural shift from “foundation to reflex” is no longer theoretical. It represents a practical, scalable strategy for deploying autonomous systems in real-world environments where both generalized intelligence and specialized control are required. In the years ahead, the autonomous systems that lead will not be those that think the best, nor those that react the fastest, but those that can do both—thinking like a human, reacting like a machine. #AutonomousVehicles #FoundationModels #ScalableAutonomy #ReflexiveControl #EndToEndAI #NextGenAutonomy #RealTimeAI #SemanticPerception #AIInnovation #IntelligentMobility

CaDeLaC - Context-Aware Deep Lagrangian Networks (DLN) for Model Predictive Control (MPC) Arxiv: https://lnkd.in/e37fcxTi DLN (example, not from this paper): https://lnkd.in/e66qn6b2 Steve Brunton DLN explanation (not from this paper): https://lnkd.in/eAyKrKx7 Physics + Learning + Real-time Control. CaDeLaC unifies Deep Lagrangian Networks (DeLaN) with online context adaptation and Model Predictive Control (MPC) to deliver zero-shot robust control under changing dynamics—like variable payloads. It’s a step toward agile, interpretable control in dynamic environments. 🦾 🔁 At a Glance 💡 Goal: Learn a physically consistent dynamics model that adapts to changing contexts in real time and integrates seamlessly with MPC. ⚙️ Approach: Extend DeLaN with residual learning to model only unknown dynamics Add context-awareness via a history-based LSTM encoder Integrate into MPC to control robots under time-varying external loads 📈 Impact (Key Metrics) 📉 39% reduction in end-effector tracking error vs. nominal model 🧠 Generalizes across 100+ payloads (0–4 kg, random CoM) 🚀 Zero-shot transfer to real robot (Franka Emika Panda) after sim-only training 🔁 Outperforms Extended Kalman Filter baseline on torque and position tracking 🔬 Experiments Robots: 7-DOF Franka Emika Panda Tasks: Joint trajectory tracking Pick-and-place with dynamic payload changes High-speed motion under varying loads (1kg–3kg) Benchmarks: MPC (nominal), EKF-MPC, CaDeLaC Metrics: Torque RMSE, position/velocity tracking, end-effector path deviation 🛠 How to Implement 1️⃣ Residual DeLaN Learn torque residuals between nominal model and real dynamics Predict mass/inertia-related errors with compact MLPs 2️⃣ Contextual Encoding Use LSTM to encode recent joint state & torque residuals Feed latent vector z into DeLaN to modulate model predictions 3️⃣ MPC Integration (CaDeLaC) Use Acados + HPIPM + CasADi for real-time optimization Update torque predictions at 50Hz, combined with 1kHz PD controller 4️⃣ Training Pipeline Collect 1M+ sim samples with randomized payloads in MuJoCo Use chirp signals for system excitation Jointly train DeLaN + LSTM offline, deploy online inference only 📦 Deployment Benefits ✅ Physics-informed learning with interpretable structure ✅ Context adaptation without retraining ✅ Data-efficient with residual modeling ✅ Real-time ready (latency <20ms) ✅ Robust to load variations, friction, and modeling mismatch 📣 Takeaway CaDeLaC is not just another neural MPC—it’s a physics-aware, context-adaptive control framework that tracks precisely even under changing dynamics. Train in sim, adapt in real-time. Control with confidence. Follow me to know more about AI, ML and Robotics!

HARRI: High-speed Adaptive Robot for Robust Interactions This video showcases some of the early testing footage of HARRI (High-speed Adaptive Robot for Robust Interactions), a next-generation proprioceptive robotic manipulator developed at the Robotics & Mechanisms Laboratory (RoMeLa) at UCLA. Designed for dynamic and force-critical tasks, HARRI leverages quasi-direct drive proprioceptive actuators combined with advanced control strategies such as impedance control and real-time model predictive control (MPC) to achieve high-speed, precise, and safe manipulation in human-centric and unstructured environments. Built with a lightweight, low-inertia structure and powered by highly back-drivable actuators, HARRI enables rapid, compliant interactions with its surroundings. By embedding proprioceptive sensing directly into the actuators, HARRI provides real-time feedback on position, velocity, and torque without relying on external sensors, greatly enhancing its adaptability and robustness in dynamic tasks. Demonstrations in this video include: • Catching a flying ball with high precision and compliant force control. • Catching a moving box, showcasing fast and adaptive manipulation of heavier and more irregular objects. • Safe direct physical interaction with a human, demonstrating compliant and controlled responses to intentional contact. • And plenty of blooper videos for fun! HARRI highlights the transition from traditional rigid position controlled robotic systems to agile, intelligent, and safe manipulators capable of working alongside humans. This research paves the way for future robotic systems that combine proprioception, real-time optimization, and adaptive control to handle increasingly complex and dynamic real-world challenges. https://lnkd.in/dR-Kpznb