We discuss a novel method for estimating task Cartesian position and velocity in robot manipulators. This is done by model-based fusion of inertial measurement units with motor encoders. The model is developed to robustly handle the uncertainties in the trajectory. Thus, not only the approach benefits from high fidelity and bandwidth thanks to multiple-sensory fusion, but it also enforces stability despite poorly formulated motions. This empowers the method to be utilized in complex closed-loop applications, where both task position and velocity information is required.

Safely generating impacts with robots is challenging due to subsequent discontinuous velocity and high impact forces. We aim at increasing the impact velocity – the robot’s relative speed prior to contact – such that impact-tasks like grabbing and boxing are made with the highest allowable speed performance when needed. Previous works addressed this problem for rigid bodies’ impacts. This letter proposes a control paradigm for generating intentional impacts with deformable contacts that incorporates hardware and task constraints. Based on data-driven learning of the shock-absorbing soft dynamics and a novel mapping of joint-space limits to contact-space, we devise a constrained model-predictive control to maximize the intentional impact within a feasible, robot-safe level. Our approach is assessed with real-robot experiments on the redundant Panda manipulator, demonstrating high pre-impact velocities (up to 0.9 m/s) of a rigid end-effector on soft objects and an end-effector soft suction-pump on rigid or deformable objects.

In recent years the need for manipulation tasks in the industrial as well as in the service robotics domain that require compliant interaction with the environment rose. Since then, an increased number of publications use a model-driven approach to describe these tasks. High-level tasks and sequences of skills are coordinated to achieve a desired motion for e.g., screwing, polishing, or snap mounting. Even though the awareness of the environment, especially in terms of contact situations, is essential for successful task execution, it is too often neglected or considered insufficiently.In this paper, we present a model-based approach, using domain-specific languages (DSL), that enables the explicit modeling of the environment in terms of contact situations. Decoupling the environment model from the skills, fosters exchangeability and allows the adaptation to different environmental situations. This way, an explicit but non-invasive link is established to the skills, enabling the environment model to provide a context to constrain the execution of the skills. Further, we present a synthesis from the modeled contact situations to a real-time component-based control architecture, which executes the skills subject to the active environmental context. A dual arm yoga mat rolling task is used to show the impact of the environment model on the skill execution.

Tracking multiple prioritized tasks simultaneously with redundant robots have been investigated extensively over the last decades. Recent research focuses on combining advantages from both classical soft and strict prioritization schemes which is non-trivial. Among the proposed methods to tackle this issue, Generalized Hierarchical Control (GHC) seems to have a reasonable performance, however, it does not include a weighting matrix in the computation of the nullspace projection operator and hence cannot construct dynamically-consistent stack-of-tasks hierarchies as a special case. We extend GHC by adding dynamic-consistency to the control scheme and refer to it as DynGHC. The extension is also advantageous when choosing non-strict priorities because inertia coupling between tasks is reduced. DynGHC allows to smoothly rearrange priorities which is important for robots acting in dynamically changing contexts. Comparative simulations with a 4 DOF planar manipulator and a KUKA LWR validate our approach. Matlab and C++ source code is made available.

We consider a virtual manipulator in grasping scenarios which allows us to capture the effect of the object dynamics. This modeling approach turns a multi-arm robot into an underactuated system. We observe that controlling floating-base multi-leg robots is fundamentally similar. The Projected Inverse Dynamics Control approach is employed for decoupling contact consistent motion generation and controlling contact wrenches. The proposed framework for underactuated robots has been evaluated on an enormous robot hand composed of four KUKA LWR IV+ representing fingers cooperatively manipulating a 9kg box with total 28 actuated DOF and six virtual DOF representing the object as additional free-floating robot link. Finally, we validate the same approach on ANYmal, a floating-base quadruped with 12 actuated DOF. Experiments are performed both in simulation and real world.

We propose a model-based control framework for multi-arm manipulation of a rigid object subject to external disturbances. The control framework, based on projected inverse dynamics, decomposes the control law into constrained and unconstrained subspaces. Unconstrained components accomplish the motion task with a desired 6-DOF Cartesian impedance behaviour against external disturbances. Meanwhile, the constrained component enforces contact and friction constraints by optimising for contact forces within the constrained subspace. External disturbances are explicitly compensated for without using force/torque sensors at the contact points. The approach is evaluated on a dual-arm platform manipulating a rigid object while coping with unknown object dynamics and human interaction.

Projection operators are widely employed in multi-objective robot control. It is an open research question how to achieve continuous transitions between different idempotent projectors which is required for dynamic task priority rearrangement. We formalize projection shaping, providing a solution to deal with rank changes in a smooth fashion. Furthermore, we derive meaningful shaping operators and show that damped least squares is a special case of our general formulation. Finally, we extend the Stack-of-Tasks prioritization scheme for continuous priority rearrangement of single task dimensions. Simulation results validate our approach.

Simultaneous mastering of multiple tasks during motion generation is challenging. Traditional null-space based approaches for redundant robots implement a strict, hierarchical prioritization for tracking multiple objectives. In consequence, these schemes are not suited to impose smooth priorities or changing them during motion execution. A recently developed mixture of controller approach superpose torques from several control modules for motion generation and thereby enables to flexibly impose priorities for pursuing different goals in parallel. The main contribution of this paper is the development of a framework which allows for automatic derivation of suitable mixture coefficients which represent priorities. The functionality of the optimization framework is demonstrated for a virtual 3 DOF pendulum and the humanoid robot COMAN.