Real-Time Robot Behavior Management

Can AI and LLMs Get Robots to Cooperate Smarter? Imagine going into a busy factory floor where robots are performing complicated tasks but also describing in real time what they're doing. This was the sci-fi dream now so well within grasp. This paper gives great insight into how LLMs and VLMs will reshape human-robot collaboration-particularly in high-consequence industries. 🔹 Research Focus Ammar N. Abbas (TU Dublin Computer Science) and Csaba Beleznai (AIT Austrian Institute of Technology) discussed how the integration of LLMs and VLMs into robotics will interpret natural language commands, understand inputs in the form of images, and explain internal processes in plain language. This approach is about creating interpretable systems, building trust, safety, and simplifying operations. 🔹 Language-Based Control LLMs are good at taking general instructions like "Pick up the red object" and turning them into very specific movements. The few-shot prompting allows learning to perform sophisticated trajectories for robots, without requiring thorough programming of the robot moves. The development decreases time spent in training and simultaneously enhances flexibility. 🔹 Context-Aware Perception By externalizing internal states, the robot alerts the operator in the event of an imminent collision or when something is missing from the environment. This form of transparency, in other words, not only builds trust but also allows to make quicker and more informed decisions, hence reducing down times and risk. 🔹 Integrating Input from Vision VLMs process sequential images to provide robots with enhanced spatial awareness. This capability enables tasks like sorting items by attributes, avoiding obstacles, and identifying safe zones for operations. 🔹 Robot Structure Awareness Equipping LLMs with knowledge of the physical structure of a robot, such as reach or mechanical limits, allows for superior task planning. For instance, it avoids overreaching and unsafe movements by robots while ensuring the accuracy and safety of the workplace. 🔹 Key Takeaway The framework illustrated industrial tasks like stacking, obstacle avoidance, and grasping through simulation in: - Accurate generation of control patterns - Real-time contextual reasoning and feedback. - Performing multi-step tasks successfully with both structural and visual data. 📌 Practical Applications   This research aims to make advanced robotics accessible to non-experts by bridging automation and collaboration. It promises faster deployment, enhanced safety, efficiency, and improved trust between human and robotic teams. 👉 How can AI and LLMs enhance decision-making in industrial robotics? What are the biggest challenges in implementing LLM-driven robotics? 👈 #ArtificialIntelligence #MachineLearning #AI #GenerativeAI #IndustrialAutomation #Robotics #SmartManufacturing Subscribe to my Newletter: https://lnkd.in/dQzKZJ79

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

"A robot without control is just a pile of metal" Building the bomb diffusion robot wasn’t just about designing a strong frame and selecting the right motors—it was about ensuring precise, real-time control for the bomb squad. In high-risk scenarios, latency, reliability, and ease of operation can mean the difference between success and disaster. Key Challenges in Remote Control Design: - Latency & Responsiveness – The robot should receive near-instant data - Multiple Control Inputs – The robot's hybrid drive system and 5 DOF arm needed a complex integration procedure - Secure & Long-Range Communication – Ensuring uninterrupted signals in complex environments - User Experience - The controls should be intuitive enough for the user to learn within minutes The Control System We Designed: - Control Communication: Radio Frequency (RF) Control - Custom-Built Remote: One Joystick - For controlling the hybrid wheel drive system Toggle Switches - For controlling the lights and cameras and switching between skid steering and Ackermann control Rotary Encoders - For precise adjustments in robotic arm positioning. We had to strike a balance between advanced features and affordability. Instead of high-end industrial controllers, we developed a custom-built embedded control system, keeping costs optimized without compromising reliability. Building a control system is always about balancing user experience, latency, and reliability. What’s the biggest challenge you’ve faced in designing a control system? Latency? Signal interference? Intuitive UI? Drop your thoughts in the comments! 🔜 Next up: Integration & Safety—bringing together mechanics, electronics, and software to create a fully functional robot. From power management to failsafe mechanisms, I’ll share how we ensured the robot operates safely and efficiently in mission-critical scenarios. Stay tuned!

Whole-Body Model-Predictive Control of Legged Robots with MuJoCo 🤖 https://lnkd.in/ezYWwrT2 This paper presents a model-predictive control (MPC) framework using iterative LQR (iLQR) with MuJoCo dynamics to achieve real-time whole-body control for quadruped and humanoid robots. Unlike Reinforcement Learning (RL), which requires extensive data and tuning, this approach enables dynamic movements with minimal sim-to-real adaptation. Why MuJoCo iLQR for Legged Robots? ✅ Real-Time Whole-Body Control: Solves complex bipedal & quadrupedal locomotion tasks efficiently. ✅ Sim-to-Real Transfer with Minimal Adjustments: Directly applies MuJoCo-based policies to real hardware. ✅ Open-Source & Reproducible: Lowers entry barriers for whole-body MPC research. Key Features 1️⃣ Iterative LQR (iLQR) for Real-Time MPC Uses MuJoCo soft contact models to approximate robot dynamics efficiently. Generates high-dimensional, whole-body control policies for quadrupeds and humanoids. 2️⃣ Minimal Sim-to-Real Adaptation Avoids manual system identification by relying on MuJoCo’s built-in physics engine. Finite-difference derivative approximation allows direct transfer to real robots. 3️⃣ Interactive MPC GUI for Hardware Deployment Provides an open-source interface for real-time control tuning. Enables live trajectory adjustments during execution. Steps to Implement Whole-Body MPC with MuJoCo 1️⃣ Simulation & Policy Training Train iLQR-based MPC policies in MuJoCo, leveraging soft contact models. 2️⃣ Real-Time Control Deployment Deploy trained policies on Unitree Go1, Go2, and H1 humanoid robots. Use low-latency state estimation from IMUs & motion capture. 3️⃣ Fine-Tuning & Performance Optimization Adjust contact modeling parameters to improve foot-ground interaction. Tune feedback gains for stable execution on hardware. Challenges & Solutions 🚧 Handling Contact Dynamics in MPC Challenge: Contact forces in legged locomotion are non-smooth & discontinuous. Solution: MuJoCo’s soft contact model ensures stable iLQR rollouts. 🚧 Computation Speed for Whole-Body Control Challenge: Traditional MPC struggles with real-time execution. Solution: Finite-difference derivatives allow fast trajectory updates. 🚧 Quadruped-to-Biped Transition Challenge: Walking on two legs requires dynamic center of mass adaptation. Solution: Time-varying LQR (TV-LQR) stabilization improves transition control. Applications 🦿 Legged Robotics: Enhances bipedal & quadrupedal locomotion for real-world deployment. 🚧 Autonomous Navigation: Enables robots to walk dynamically across varied terrain. 🏗 Industrial & Assistive Robotics: Improves agile movement for humanoid assistance. This work revolutionizes whole-body MPC by making high-performance legged locomotion more accessible, reproducible, and scalable. Follow me to know more abotu ML, AI and Robotics

🔴 NEW ARTICLE: "VERSES AI Leads Active Inference Breakthrough in Robotics." My latest article breaks down VERSES' newest research paper titled, “Mobile Manipulation with Active Inference for Long-Horizon Rearrangement Tasks,” that was oh-so-quietly released to the public a few weeks ago (Shhh 🤫 ) This new research, led by Dr. Karl Friston's team at VERSES is the blueprint for a new robotics control stack that achieves an inner-reasoning architecture comprised of a hierarchy of multiple active inference agents within a single robot body, all working together for whole-body control to adapt and learn from moment to moment in unfamiliar environments without any offline training. ◼️ Key Takeaways: Instead of a single, monolithic Reinforcement Learning (RL) policy, their architecture creates a hierarchy of intelligent agents inside the robot, each running on the principles of Active Inference and the Free Energy Principle, outperforming current robotic paradigms on efficiency, adaptability, and safety - without the data and maintenance burden of reinforcement learning. Here’s what’s different: 🔸 Agents at Every Scale - Every joint in the robot’s body has its own “local” agent, capable of reasoning and adapting in real time. These feed into limb-level agents (e.g., arm, gripper, mobile base), which in turn feed into a whole-body agent that coordinates movement. Above that sits a high-level planner that sequences multi-step tasks. 🔸 Real-Time Adaptation - If one joint experiences unexpected resistance, the local agent adjusts instantly, while the limb-level and whole-body agents adapt the rest of the motion seamlessly — without halting the task. 🔸 Skill Composition - The robot can combine previously learned skills in new ways, enabling it to improvise when faced with novel tasks or environments. 🔸 Built-In Uncertainty Tracking - Active Inference agents model what they don’t know, enabling safer, more cautious behavior in unfamiliar situations. The result: a robot that can walk into an environment it has never seen before, understand the task, and execute it — adapting continuously as conditions change. VERSES’ broader research stack ties this directly into scalable, networked intelligence with AXIOM, Variational Bayes Gaussian Splatting (VBGS), and the Spatial Web Protocol. Together, these form the technical bridge from a single robot as a teammate to globally networked, distributed intelligent systems, where every human, robot, and system can collaborate through a shared understanding of the world. The levels of interoperability, optimization, cooperation, and co-regulation are unprecedented and staggering. Every industry will be touched by this technology. Smart cities all over the globe will come to life through this technology. ➡️ Get the full story here: 🔗 https://lnkd.in/ghFizkhn #ActiveInferenceAI #AXIOM #VBGS #Robotics #VERSESAI