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 paper presents a real-time control framework for consensus in operational space for robotic manipulators while satisfying task and input constraints. Consensus in operational space, as compared to joint space, enables heterogeneous robotic manipulators to achieve consensus. However, traditional frameworks tend to ignore task and input constraints while achieving consensus in operational space. We address this problem by defining safe sets in operational space and then ensure task constraint by designing Control Barrier Functions (CBF) in operational space. Control barrier functions guarantees to provide collision-free behavior for the robotic manipulator by modifying the nominal controller in a minimally invasive manner such that the trajectory of the manipulator remains in the safe set. The Quadratic Programming (QP) formulation also ensures that the nominal controller is only modified when the constraints are active, and the resulting controller is optimal in a min-norm setting. Our approach contrasts the traditional potential field method, which continues to influence the nominal controller because of its attractive and repulsive field design, and is therefore unsuitable for consensus problems. We also incorporate the input constraint in our QP formulation to ensure that the resulting controller complies with the task and input constraints. We show the efficacy of the proposed approach on 7 Degree of Freedom (DoF) KUKA LBR iiwa, 6 DoF KUKA KR5 R650 and 7 DoF Flexiv Rizon robotic manipulators, each with different dynamical and kinematic models using Dynamic Animation and Robotics Toolkit (DART) physics engine.

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.

Riemannian Motion Policies (RMPs) have recently been introduced as a way to specify second-order motion policies defined on robot task spaces. RMP-based approaches have the advantage of being more general than traditional approaches based on operational space control; for example, the generalized task inertia in an RMP can be fully state-dependent, which is particularly effective in designing collision avoidance bahaviors. But until now RMPs have been applied only to fully actuated systems, i.e. systems for which each degree of freedom (DoF) can be directly actuated by a control input. In this paper, we present a method that extends the RMP formalism to a class of underacutated systems whose dynamics are amenable to a decomposition into a fully-actuated subsystem and a residual dynamics. We show the efficacy of the approach by constructing a suitable decomposition for a Wheeled-Inverted-Pendulum (WIP) humanoid robot and applying our method to derive motion policies for combined locomotion and manipulation tasks. Simulation results are presented for a 7-DoF system with one degree of underactuation.

We present a hierarchical framework for trajectory optimization and optimal feedback whole-body control of wheeled inverted pendulum (WIP) humanoid robot. The framework extends rapidly exponentially stabilizing control Lyapunov functions (RES-CLF) to operational space for controlling WIP humanoid robots while utilizing a hierarchical framework to compute an optimal policy. The upper level of the hierarchy encodes locomotion tasks, while the lower level incorporates the full system dynamics, including manipulation tasks to be performed. The framework computes an optimal policy directly in the operational space. Thus it avoids computing inverse kinematics or inverse dynamics explicitly. The framework can handle torque and task constraints while guaranteeing exponential convergence and min-norm control from RES-CLF. The efficacy of the framework is demonstrated on 18 degrees of freedom (DoF) WIP humanoid robot, Golem Krang, and 7 DoF planar WIP humanoid robot.

An optimal data transmission and battery charging policy for sensor networks, with solar powered nodes, is proposed. A sensor node with multiple battery charging levels and solar radiation states is considered. To model the sensor node behavior we employ constrained Markov decision process, which achieves an optimal stochastic policy, allowing sensor node to prolong its battery lifetime with minimum data transmission rate constraint. The Markov decision process based model is evaluated using reported energy measurements for the existing platforms. The performance evaluation results reveal, how the data transmission rate should be controlled for different data arrival rates, while taking into account the current battery state and the solar radiation pattern. The proposed model is flexible and can accommodate different number of charging/discharging levels as well as radiation patterns.