This paper is about generating motion plans for high degree-of-freedom systems that account for both static and dynamic collisions along the entire body. A particular class of mathematical programs with complementarity constraints become useful in this regard. Optimization-based planners can tackle confined space trajectory planning while being cognizant of robot and (mostly static) obstacle constraints. However, handling moving obstacles is non-trivial in a real-time setting. To this end, we present the FLIQC (Fast LInear Quadratic Complementarity based) motion planner. Our reactive planner employs a novel motion model that captures the entire rigid robot as well as the obstacle geometry and ensures nonpenetration between the surfaces due to the imposed constraint. We perform thorough comparative studies with the state-of-the-art, which demonstrate improved performance. Extensive simulation and hardware experiments validate our claim of generating continuous and real-time motion plans at 1 kHz for modern collaborative robots with constant minimal parameters.

This work focuses on the agile transportation of liquids with robotic manipulators. In contrast to existing methods that are either computationally heavy, system/container specific or dependant on a singularity-prone pendulum model, we present a real-time slosh-free tracking technique. This method solely requires the reference trajectory and the robot’s kinematic constraints to output kinematically feasible joint space commands. The crucial element underlying this approach consists on mimicking the end-effector’s motion through a virtual quadrotor, which is inherently slosh-free and differentially flat, thereby allowing us to calculate a slosh-free reference orientation. Through the utilization of a cascaded proportional-derivative (PD) controller, this slosh-free reference is transformed into task space acceleration commands, which, following the resolution of a Quadratic Program (QP) based on Resolved Acceleration Control (RAC), are translated into a feasible joint configuration. The validity of the proposed approach is demonstrated by simulated and real-world experiments on a 7 DoF Franka Emika Panda robot.

This paper investigates the problem of effective tool manipulation for motion planning in complex human-like scenarios. Vector-field-based real-time strategies, although widely used, usually do not account for unwieldy tools or incorporate systematic methods to handle these extra maneuvers needed. Instead, we formalize the problem and propose a novel field-based reactive planner that explicitly accounts for rotational forces for seamless maneuvers based on the tool’s geometry and featured points. Furthermore, we capture and encode robot performance through capability metrics and improve the same using an additional quality distribution method. This enables seamless integration of the robot’s embodiment with the reactive force-torque (wrench) field giving rise to flexible tool usage in non-stationary environments. Extensive simulation analysis on a 7 DoF collaborative robot manipulating a common tool in an unorganized table-top layout reinforces our claim of robustness in stationary and non-stationary scenarios.

This paper introduces a comprehensive user-guided planning framework designed for robots operating in dynamic, human-centered environments – where the ability to execute sequential tasks flexibly and adaptively is paramount. Our planner enables robots to (i) encode object-centric constraints and user preferences via multiple demonstrations, (ii) transfer geometric features and implicit relaxations to novel scenarios while reacting to unforeseen events, and (iii) adapt to changing task conditions in real-time, including the real-time replanning and tracking of moving targets. Our approach relies on C1screw linear interpolation, which generates smooth paths satisfying the underlying geometric constraints that characterize the task. The prescribed path is combined with a hierarchical quadratic programming-based controller which explores the user demonstrations's stochastic variability to relax task constraints while ensuring real-time whole-body collision avoidance. Our framework continuously checks for dynamic changes in task targets, ensuring appropriate planning or control actions, and tending to the prescribed screw path. This comprehensive approach is deployed in different task conditions which are available at https://youtu.be/F0cMr1n1D9k.

A typical manipulation task consists of a manipulator equipped with a gripper to grasp and move an object with constraints on the motion of the hand-held object, which may be due to the nature of the task itself or from object-environment contacts. In this paper, we study the problem of computing joint torques and grasping forces for time-optimal motion of an object, while ensuring that the grasp is not lost and any constraints on the motion of the object, either due to dynamics, environment contact, or no-slip requirements, are also satisfied. We present a second-order cone program (SOCP) formulation of the time-optimal trajectory planning problem that considers nonlinear friction cone constraints at the hand-object and object-environment contacts. Since SOCPs are convex optimization problems that can be solved optimally in polynomial time using interior point methods, we can solve the trajectory optimization problem efficiently. We present simulation results on three examples, including a non-prehensile manipulation task, which shows the generality and effectiveness of our approach.

Future robots operating in fast-changing anthropomorphic environments need to be reactive, safe, flexible, and intuitively use both arms (comparable to humans) to handle task-space constrained manipulation scenarios. Furthermore, dynamic environments pose additional challenges for motion planning due to a continual requirement for validation and refinement of plans. This work addresses the issues with vector-field-based motion generation strategies, which are often prone to local-minima problems. We aim to bridge the gap between reactive solutions, global planning, and constrained cooperative (two-arm) manipulation in partially known surroundings. To this end, we introduce novel planning and real-time control strategies leveraging the geometry of the task space that are inherently coupled for seamless operation in dynamic scenarios. Our integrated multiagent global planning and control scheme explores controllable sets in the previously introducedcooperative dual task spaceand flexibly controls them by exploiting the redundancy of the high degree-of-freedom (DOF) system. The planning and control framework is extensively validated in complex, cluttered, and nonstationary simulation scenarios where our framework is able to complete constrained tasks in a reliable manner, whereas existing solutions fail. We also perform additional real-world experiments with a two-armed 14 DOF torque-controlled KoBo robot. Our rigorous simulation studies and real-world experiments reinforce the claim that the framework is able to run robustly within the inner loop of modern collaborative robots with vision feedback.

As robots and humans increasingly share the same workspace, the development of safe motion plans becomes paramount. For real-world applications, nonetheless, it is critical that safety solutions are achieved without compromising performance. The computation of safe, time-efficient trajectories, however, usually requires rather complex often decoupled planning and optimization methods which degrades the nominal performance. In this work, instead, we cast the problem as a graph search-based scheme that enables us to solve the problem efficiently. The graph search is guided by an informed cost balance criterion. In this context we present the S* algorithm which minimizes the total planning time by equilibrising shortest time-efficient paths and paths with higher safe velocities. The approach is compatible with standards and validated both in rigorous simulation trials on a 6 DoF UR5 robot as well as real world experiments on a Franka Emika 7 DoF research robot.

Shared-autonomy control strategies in teleoperation combine human decision-making and robot precision to solve complex tasks. In other words, advanced autonomous control algorithms can compensate for imprecise human commands, reduce the mental workload of the user, and enable the execution of tasks that otherwise wouldn't be feasible. This paper addresses one of these previously challenging scenarios. Herein, we present a novel control framework and motion generator that allows for real-time non-prehensile slosh-free teleoperation of liquids. The proposed approach is able to generate robust trajectories on the follower side which ensures task-space, joint-space, and manipulability constraint satisfaction. Our findings were evaluated through user studies and real-world scenarios. Participants were even explicitly challenged to try to spill liquid through teleoperation, reaching speeds up to 0.6 m/s.

As collaborative robots move closer to human environments, motion generation and reactive planning strategies that allow for elaborate task execution with minimal easy-to-implement guidance whilst coping with changes in the environment is of paramount importance. In this paper, we present a novel approach for generating real-time motion plans for point-to-point tasks using a single successful human demonstration. Our approach is based on screw linear interpolation, which allows us to respect the underlying geometric constraints that characterize the task and are implicitly present in the demonstration. We also integrate an original reactive collision avoidance approach with our planner. We present extensive experimental results to demonstrate that with our approach, by using a single demonstration of moving one block, we can generate motion plans for complex tasks like stacking multiple blocks (in a dynamic environment). Analogous generalization abilities are also shown for tasks like pouring and loading shelves. For the pouring task, we also show that a demonstration given for one-armed pouring can be used for planning pouring with a dual-armed manipulator of different kinematic structure.

This paper is about fast slosh-free fluid transportation. Existing approaches are either computationally heavy or only suitable for specific robots and container shapes. We model the end effector as a point mass suspended by a spherical pendulum and study the requirements for slosh-free motion and the validity of the point mass model. In this approach, slosh-free trajectories are generated by controlling the pendulum's pivot and simulating the motion of the point mass. We cast the trajectory optimization problem as a quadratic program-this strategy can be used to obtain valid control inputs. Through simulations and experiments on a 7 DoF Franka Emika Panda robot we validate the effectiveness of the proposed approach.

This paper addresses the problem of real-time planning in constrained dual-arm manipulation scenarios. Our proposed coupling leverages manipulability information of the cooperative bimanual task-space to a vector-field based planner by means of a repulsive circulatory field, while geometric primitives in Spin(3)⋉ℝ3are explored for flexible task relaxation. Furthermore, the circular field informs the cooperative framework about the safety boundaries which are in turn used to further relax motion constraints within a collision-free ball in Cartesian space. This builds a funnel along the trajectory which can be directly tracked through the proposed switching of task-primitive-priorities. The switching strategy follows an approach that ensures robustness to chattering and continuity in the joint-space. Experiments verify that our framework can run within the inner control loop of Franka Emika Panda robots.

Similar to human work, robotic tasks sometimes require two hands to be accomplished. This requires coordinated motion planning and control. While fulfilling the task in a coordinated manner is already a big challenge, the task at hand becomes even harder when obstacles are introduced in the environment that need to be avoided. Furthermore in the case of dynamic environments, contacts cannot be avoided all the time, even with robust planning. In addition to geometric constraints, bimanual systems need to be able to detect and react to contacts during task execution. To this aim, we integrate a vector-field based planning scheme, that is able to avoid obstacles, with contact detection and reactive control methods based on contact wrench estimation such as admittance control. We also fuse the real contact forces into the planner directly together with the circular repulsive fields. The resulting planner-controller combination is capable of obstacle avoidance planning as well as reaction control in the case of unforeseen contacts that can also be used in situations where the manipulation needs to be guided by the environment such as landing control in only roughly known environments. We evaluate our approach on the torque-controlled Kobo bimanual set-up and also perform rigorous simulation studies.