The integration of large language models (LLMs) into robotic task planning has unlocked better reasoning capabilities for complex, long-horizon workflows. However, ensuring safety in LLM-driven plans remains a critical challenge, as these models often prioritize task completion over risk mitigation. This paper introduces SAFER (Safety-Aware Framework for Execution in Robotics), a multi-LLM framework designed to embed safety awareness into robotic task planning. SAFER employs a Safety Agent that operates alongside the primary task planner, providing safety feedback. Additionally, we introduce LLM-as-a-Judge, a novel metric leveraging LLMs as evaluators to quantify safety violations within generated task plans. Our framework integrates safety feedback at multiple stages of execution, enabling real-time risk assessment, proactive error correction, and transparent safety evaluation. We also integrate a control framework using Control Barrier Functions (CBFs) to ensure safety guarantees within SAFER’s task planning. We evaluated SAFER against state-of-the-art LLM planners on complex long-horizon tasks involving heterogeneous robotic agents, demonstrating its effectiveness in reducing safety violations while maintaining task efficiency. We also verify the task planner and safety planner through actual hardware experiments involving multiple robots and a human.

To enable a mobile manipulator to effectively maneuver a cart, we derive a dynamic model for the cart that incorporates the nonholonomic constraints on its motion, and use this model to formulate an estimator for the cart's inertial parameters. By deriving the dynamic equations of the cart using a constrained Euler-Lagrange formulation, we are able to directly incorporate nonholonomic constraints into the dynamics in a way that is independent of the kinematic parameters of the cart (e.g., specific wheel configuration, wheel radius, etc.), eliminating the need to either calibrate or estimate these kinematic parameters. We then construct an extended Kalman filter (including an explicit calculation of the linearized system and observation matrices) that uses an augmented state representation to estimate the cart's inertial parameters. We validate our approach both in simulation and experimentally using a mobile manipulator to maneuver a typical shopping cart. These experiments confirm the accuracy of our estimator, show that accurate estimation of the inertial parameters can significantly reduce the force/torque needed to successfully control the system, and illuminate the effects of varying the contact points at which the mobile manipulator applies forces and torques to guide the cart along a desired trajectory.

This work focuses on the capability of Mobile Manipulators to effectively control and maneuver cart-like non-holonomic systems. These cart-like systems are passive-wheeled objects with nonholonomic constraints with varying inertial parameters. We derive the dynamic equations of the cart-like system using a constrained Euler-Lagrange formulation and propose a Linear Quadratic Regulator controller to move the cart along a desired trajectory using external forces (applied by the MM) at a given contact point. For the MM, we present a control architecture to i) control the mobile base to keep the cart inside the workspace of the manipulator and ii) a control Lyapunov function formulation to control the manipulator in torque control, while decoupling the motion of the base from the arm and applying the required wrench onto the object. We validate our approach experimentally, using a MM to push a shopping cart and track desired trajectories. These experiments show the accuracy of the control architecture to track the desired trajectories for carts with different inertial parameters and improve the controllability of the system by changing the contact point on the cart.

In this paper, we study the problem of dynamic interaction between a robot and an unknown object (e.g., catching a ball, or handing off an object during locomotion). In particular, we propose a method for estimating the inertial parameters of an object during dynamic interaction, while minimally altering the trajectory of the object – a minimal interaction approach. Our method combines trajectory estimation (e.g., using standard methods from computer vision) with a model-based estimator that exploits the robot’s known dynamic model. We first develop the method for a generalized three-dimensional problem, and then evaluate the method for the case of an object moving along a linear trajectory. We present experimental results obtained using a KUKA iiwa 7 interacting with rolling balls of varying mass. Our experiments demonstrate that the mass of the objects can be accurately estimated at the moment of impact when accurate object trajectory estimates are available, and that significant improvement can be obtained by incorporating force measurements at the contact point while following the object.

This paper presents a real-time safety and control for robot manipulators using control barrier functions and control Lyapunov functions in operational space. We first define the operational space in terms of system dynamics, jacobian, and torques and then ensure safety by designing Control Barrier Functions (CBF) around the body links of the robotic manipulator. The control barrier function provides provable collision-free behavior for the robotic manipulator by modifying the nominal control in a minimally invasive manner to formally satisfy the safety constraints. CBFs are formulated as a quadratic programming problem, which can be solved in real-time. We also design a controller based on Rapidly Exponentially Stabilizing Control Lyapunov Function (RESCLF) and quadratic programming to meet multiple objectives while ensuring exponential convergence. We then extend our formulation to solve RESCLF and CBF in a unified formulation to design the controller while ensuring the safety of manipulators and guaranteeing the torque saturation. The efficacy of the proposed approach is shown on 7 Degree of Freedom (DoF) KUKA LBR iiwa robot using Dynamic Animation and Robotics Toolkit (DART) physics engine.

The present work models the dynamics of general skid-steer mobile manipulators using the formalism and tools of the spatial vectors algebra introduced by Featherstone. The model built is validated using inertial measurements obtained during field tests with a compact skid-steer loader. The paper demonstrates the benefits of using the spatial vector algebra formulation, showing that this modeling approach allows to integrate traction forces and study the arm-vehicle, as well as vehicle-ground interactions in a single model. This feature is not possible with many other of the existing modeling approaches and simulation tools, thus opens the way to research on mechanically more complex robot designs and their controllers. It is to be noted that most of the existing models and simulations of mobile manipulators consider two-wheeled differentially driven bases and avoid accurate models of skid-steering bases because of the complexity of simulating wheels that skid while rolling. However, skid-steer traction is common in most of the industrial construction and mining machinery because of their simpler mechanics, high reliability, and better mobility in rough terrains. Hence, the development of physically accurate models of skid-steer manipulators is fundamental. We chose to validate the model using a Cat®262C compact-skid steer loader instead of a small mobile manipulator common in robotics research laboratory to highlight the usefulness of the presented model and the spatial vector algebra approach.