We present a distributed, event-triggered, and adaptive control algorithm for cooperative object manipulation withrolling contacts and unknown dynamic parameters. Whereasconventional cooperative manipulation methods require rigidcontact points, our approach exploits rolling effects of passiveend-effectors and does not require force/torque sensing. Theremoval of rigidity allows for more modular grasping, increasedapplication to more object types, and online adjustment ofthe grasp. The proposed control algorithm exhibits the following properties. Firstly, it is distributed, in the sense thatthe robotic agents calculate their own control signal, under anevent-triggered communication scheme. Such a scheme reducesthe inter-agent communication requirements with respect tocontinuous communication schemes. Secondly, it uses an onlineadaptation mechanism to accommodate for unknown dynamicparameters of the object and the agents. Finally, it adaptsexisting internal force controllers to guarantee no slip throughoutthe manipulation task despite the event-triggered nature of thecommunication scheme. Hardware implementation validates theeffectiveness of the proposed approach.

We propose a novel (Type-II) zeroing control barrier function (ZCBF) for safety-critical control, which generalizes the original ZCBF approach. Our method allows for applications to a larger class of systems (e.g., passivity-based) while still ensuring robustness, for which the construction of conventional ZCBFs is difficult. We also propose a locally Lipschitz continuous control law that handles multiple ZCBFs, while respecting input constraints, which is not currently possible with existing ZCBF methods. We apply the proposed concept for unicycle navigation in an obstacle-rich environment.

The work presented here is a culmination of developments within the Swedish project COIN: Co-adaptive human-robot interactive systems, funded by the Swedish Foundation for Strategic Research (SSF), which addresses a unified framework for co-adaptive methodologies in human-robot co-existence. We investigate co-adaptation in the context of safe planning/control, trust, and multi-modal human-robot interactions, and present novel methods that allow humans and robots to adapt to one another and discuss directions for future work.

We present a framework for safe and optimal trajectory tracking by combining Model Predictive Control and Sampled-Data Control Barrier functions. This framework, which we call Corridor MPC, safely and robustly keeps the state of the system within a corridor that is defined as a permissible error around a reference trajectory. By incorporating Sampled-Data Control Barrier functions into an MPC framework, we guarantee safety for the continuous-time system in the sense of staying within the corridor and practical stability in the sense of converging to the reference trajectory. The proposed framework is evaluated with a free-flyer kinematics system.

In this paper, we propose a notion of high-order (zeroing) barrier functions that generalizes the concept of zeroing barrier functions and guarantees set forward invariance by checking their higher order derivatives. The proposed formulation guarantees asymptotic stability of the forward invariant set, which is highly favorable for robustness with respect to model perturbations. No forward completeness assumption is needed in our setting in contrast to existing high order barrier function methods. For the case of controlled dynamical systems, we relax the requirement of uniform relative degree and propose a singularity-free control scheme that yields a locally Lipschitz control signal and guarantees safety. Furthermore, the proposed formulation accounts for ``performance-critical" control: it guarantees that a subset of the forward invariant set will admit any existing, bounded control law, while still ensuring forward invariance of the set. Finally, a non-trivial case study with rigid-body attitude dynamics and interconnected cell regions as the safe region is investigated.

In this paper, we propose a ROS software package for planning and control of robotic systems with a human-in-the-Ioop focus. The software uses temporal logic specifications, specifically Linear Temporal Logic, for a language-based method to develop correct-by-design high level robot plans. The approach is structured to allow a human to adjust the high-level plan online. A human may also take control of the robot (in a low-level control fashion), but the software prevents the human from implementing dangerous behaviour that would violate the high-level task specification. Finally, the planner is able to learn human-preferred high-level tasks by tracking human low-level control inputs in an inverse learning framework. The proposed approach is demonstrated in a warehouse setting with multiple robot agents to showcase the efficacy of the proposed solution.

Control barrier functions have been demonstrated to be a useful method of ensuring constraint satisfaction for a wide class of controllers. However, the existing results are mostly restricted to continuous-time systems. Mechanical systems, including robots, are typically second-order systems in which the control occurs at the force/torque level. These systems have actuator, velocity, and position constraints (i.e., relative degree two) that are vital for safety and/or task execution. Additionally, mechanical systems are typically controlled digitally as sampled-data systems. The contribution of this article is twofold. The first contribution is the development of novel, robust control barrier functions that ensure constraint satisfaction for sampled-data systems in the presence of model uncertainty and allows for satisfaction of actuator constraints. The second contribution is the application of the proposed method to the challenging problem of robotic grasping in which a robotic hand must ensure that an object remains inside the grasp while manipulating it to the desired reference trajectory. A grasp constraint satisfying controller is proposed that can admit the existing nominal manipulation controllers from the literature while simultaneously ensuring no slip, no overextension (e.g., singular configurations), and no rolling off of the fingertips. Simulation and experimental results validate the proposed control for the robotic hand application. 

In this paper, we propose a novel safe, passive, and robust control law for mechanical systems. The proposed approach addresses safety from a physical human-robot interaction perspective, where a robot must not only stay inside a pre-defined region, but respect velocity constraints and ensure passivity with respect to external perturbations that may arise from a human or the environment. The proposed control is written in closed-form, behaves well even during singular configurations, and allows any nominal control law to be applied inside the operating region as long as the safety requirements (e.g., velocity) are adhered to. The proposed method is implemented on a 6-DOF robot to demonstrate its effectiveness during a physical human-robot interaction task.

In this paper we present a novel adaptive cooperative manipulation controller for multiple mobile robots with rolling contacts. Our approach exploits rolling effects of passive end-effectors and does not require force/torque sensing. Moreover, the proposed scheme is robust to uncertain dynamics of the object and agents including object center of mass, inertia, weight, and Coriolis terms. In addition, we present a novel closed-form internal force controller that guarantees no slip throughout the manipulation task. The adaptive controller design ensures boundedness of the estimated model parameters in predefined sets. Numerical simulations validate the effectiveness of the proposed approach.

Control barrier functions are valuable for satisfying system constraints for general nonlinear systems. However a main drawback to existing techniques is the proper construction of these barrier functions to satisfy system and input constraints. In this paper, we propose a methodology to construct control barrier functions for Euler-Lagrange systems subject to input constraints. The proposed approach is validated in simulation on a 2-DOF planar manipulator.