Minimizing Dynamics Mismatch in Robotics Systems

Port-Hamiltonian Neural ODE Networks on Lie Groups For Robot Dynamics Learning and Control https://lnkd.in/evFW4BUz Thai Duong, Abdullah Altawaitan, Jason Stanley, Nikolay Atanasov Accurate models of robot dynamics are critical for safe and stable control and generalization to novel operational conditions. Hand-designed models, however, may be insufficiently accurate, even after careful parameter tuning. This motivates the use of machine learning techniques to approximate the robot dynamics over a training set of state-control trajectories. The dynamics of many robots are described in terms of their generalized coordinates on a matrix Lie group, e.g. on SE(3) for ground, aerial, and underwater vehicles, and generalized velocity, and satisfy conservation of energy principles. This paper proposes a (port-)Hamiltonian formulation over a Lie group of the structure of a neural ordinary differential equation (ODE) network to approximate the robot dynamics. In contrast to a black-box ODE network, our formulation guarantees energy conservation principle and Lie group's constraints by construction and explicitly accounts for energy-dissipation effect such as friction and drag forces in the dynamics model. We develop energy shaping and damping injection control for the learned, potentially under-actuated Hamiltonian dynamics to enable a unified approach for stabilization and trajectory tracking with various robot platforms.

This paper employs viability theory to pre-compute safe sets in the state-space of joint positions and velocities. These viable sets, constructed via data-driven and analytical methods for self-collision avoidance, external object collision avoidance and joint-position and joint-velocity limits, provide constraints on joint accelerations and thus joint torques via the robot dynamics. A quadratic programming-based control framework enforces these constraints on a passive controller tracking a dynamical system, ensuring the robot states remain within the safe set in an infinite time horizon. The proposed approach is validated through simulations and hardware experiments on a 7-DoF Franka Emika manipulator. In comparison to a baseline constrained passive controller, this method operates at higher control-loop rates and yields smoother trajectories. #research: https://vpp-tc.github.io #authors: Zizhe Zhang, Yicong Wang, Zhiquan Zhang, Tianyu Li, Nadia Figueroa

Minimizing Sim2real Error in Robotics when deploying to Real World: Maybe the best open source content available on internet. This content is written directly by the Reinforcement Learning and Imitation Learning team at Limx Dynamics. https://lnkd.in/gFxXSCYe Deployment: When deploying a trained policy to the real robot, we often encounter stable simulation performance but markedly different real-world behavior – the Sim2Real Gap. This is a common challenge in robot locomotion training. When facing a significant Gap, we need to check if the following two aspects have been properly addressed: 1. Build accurate robot models: 2. Establish an accurate simulation environment: If a large gap persists after checking these conditions, we need to analyze whether our policy is overfitting, meaning it's only applicable within a narrow range of environments. In this case, focus on the appropriateness of Domain Randomization. Adding randomization items or increasing the randomization range can enhance the model's generalization performance. After optimization, the open-source algorithm's performance on the real robot closely approaches that of the deeply optimized algorithm in remote control mode. Opportunities for Further Optimization The algorithm developed this time is implemented based on the CTS framework. To further enhance performance, researchers are encouraged to explore the following directions in-depth: 1. CTS framework enhancement: Improve the CTS framework on multiple levels, such as innovatively designing the Teacher Policy mechanism, using knowledge distillation to improve adaptability in complex terrain, and incorporating insights into robot dynamics into the Student Policy. 2. State representation optimization: The current version explicitly models the robot's linear velocity. Further work could expand implicit feature learning from historical observation data. 3. Improved network architecture: Upgrade the existing MLP policy network to a sequence-aware architecture (e.g., CNN or GRU) to better handle temporal dependencies in motion control. 4.Introducing more observations: Incorporating sensor data like vision can further enrich the robot's environmental perception. Introduce other generative models: Experiment with methods like diffusion models to generate longer action sequence commands. Click here for the website: https://lnkd.in/gFxXSCYe Sales: Gigi YE Cofounder: Li Zhang Github: https://lnkd.in/gpAUqMbZ https://lnkd.in/gFxXSCYe Detailed article: https://lnkd.in/gD-F9iGp Paper:https://lnkd.in/gz7Wp64P #Robotics #BipedalRobot #TRON1 #ReinforcementLearning #RL #EmbodiedIntelligence #AI #MachineLearning #OpenSource #SDK #Simulation #Sim2Real #IsaacGym #CTS #RobotLocomotion #Algorithm #LimX #TechInnovation #Python #ControlSystems

Agile by Adaptation: Quadrotor Flight Perfected with L1-NMPC "Performance, Precision, and Payloads: Adaptive Nonlinear MPC for Quadrotors" Introduces L1-NMPC, a hybrid adaptive nonlinear model predictive control for quadrotors. It addresses model uncertainties like aerodynamic effects, payload variations, and parameter mismatches. Learns and compensates for uncertainties online, achieving a 90% reduction in tracking errors under disturbances compared to non-adaptive NMPC. Demonstrates exceptional performance in diverse environments, including windy conditions, unknown payloads, and agile racing trajectories at speeds up to 70 km/h. Minimizes computational overhead and eliminates the need for gain tuning, offering flexibility, precision, and robustness for demanding quadrotor applications. Video - https://lnkd.in/epAzUP7D Paper - https://lnkd.in/ei4mBvqs If you are an aspiring Roboticist, -------------------------------- Join my WhatsApp Robotics Channel - https://lnkd.in/dYxB9iCh Join our Robotics Community - https://lnkd.in/e6twxYJF Watch my Podcast - https://lnkd.in/eaX2yDSM -------------------------------- #robotics