Marco M. Nicotra

Victor Freire, Marco M. Nicotra

This paper presents a systematic approach to construct control barrier functions for nonlinear control affine systems subject to arbitrary state and input constraints. Taking inspiration from the reference governor literature, the proposed method defines a family of backup policies, parametrized by the equilibrium manifold of the system. The control barrier function is defined on the augmented state-and-reference space: given a state-reference pair, the approach quantifies the distance to constraint violation at any time in the future. The proposed method is applied to an inverted pendulum on cart.

Yaashia Gautam, Gilberto Briscoe-Martinez, Adhitya Mohan et al.

This paper introduces the Compliant Explicit Reference Governor (CERG), a modular reference management system that enables robots to interact physically with their environment under provable guarantees. The CERG is an intermediate layer that can be placed between a high-level planner and a low-level controller: it enforces operational constraints and enables smooth transitions between free-motion and contact operations. The CERG ensures safety by limiting the total energy available to the robotic arm at the time of contact. In the absence of contact, however, the CERG does not penalize the system performance. Simulation and hardware experiments validate the CERG on increasingly complex systems.

Marco M. Nicotra, Dominic Liao-McPherson, Ilya V. Kolmanovsky

This paper introduces a continuous-time constrained nonlinear control scheme which implements a model predictive control strategy as a continuous-time dynamic system. The approach is based on the idea that the solution of the optimal control problem can be embedded into the internal states of a dynamic control law which runs in parallel to the system. Using input to state stability arguments, it is shown that if the controller dynamics are sufficiently fast with respect to the plant dynamics, the interconnection between the two systems is asymptotically stable. Additionally, it is shown that, by augmenting the proposed scheme with an add-on unit known as an Explicit Reference Governor, it is possible to drastically increase the set of initial conditions that can be steered to the desired reference without violating the constraints. Numerical examples demonstrate the effectiveness of the proposed scheme.

Victor Freire, Marco M. Nicotra

This paper presents an approach to design control barrier functions (CBFs) for arbitrary state and input constraints using tools from the reference governor literature. In particular, it is shown that dynamic safety margins (DSMs) are CBFs for an augmented system obtained by concatenating the state with a virtual reference. The proposed approach is agnostic to the relative degree and can handle multiple state and input constraints using the control-sharing property of CBFs. The construction of CBFs using Lyapunov-based DSMs is then investigated in further detail. Numerical simulations show that the method outperforms existing DSM-based approaches, while also guaranteeing safety and persistent feasibility of the associated optimization program.

Tam W. Nguyen, Marco M. Nicotra, Emanuele Garone

This technical note focuses on the control of a quadrotor unmanned aerial vehicle (UAV) tethered to the ground. The control objective is to stabilize the UAV to the desired position while ensuring that the cable remains taut at all times. A cascade control scheme is proposed. The inner loop controls the attitude of the UAV. The outer loop gives the attitude reference to the inner loop, and is designed so that (i) the gravity force is compensated, (ii) the cable is taut at all times, and (iii) the trajectory of the UAV follows the geodesic path. To prove asymptotic stability, small gain arguments are used. The control scheme is augmented with a reference governor to enforce constraints.

Marco M. Nicotra, Tam Nguyen, Emanuele Garone et al.

This paper introduces an explicit reference governor approach for controlling time delay linear systems subject to state and input constraints. The proposed framework relies on suitable invariant sets that can be built using both Lyapunov-Razumikhin and Lyapunov-Krasovskii arguments. The proposed method is validated both numerically and experimentally using several alternative formulations.

Simon J. Jones, Dominic Liao-McPherson, Marco M. Nicotra

This paper details a novel indirect method for solving constrained optimal control problems (OCPs) directly in continuous-time function space. The KKT conditions are embedded in a non-smooth complementarity function, which enables their reformulation as a rootfinding problem in Banach space. This problem is then solved using a non-smooth Newton method. Finally, the paper shows that the Newton update can be obtained by solving a modified differential Riccati equation, where the cost terms are reweighted at every iteration based on the constraint multipliers. Numerical simulations show the effectiveness of the method, which converges superlinearly up to the tolerance of the ODE solver.

Gilberto G. Briscoe-Martinez, Yaashia Gautam, Rahul Shetty et al.

Robot failure is detrimental and disruptive, often requiring human intervention to recover. Maintaining safe operation under impairment to achieve task completion, i.e. fail-active operation, is our target. Focusing on actuation failures, we introduce DEFT, a diffusion-based trajectory generator conditioned on the robot's current embodiment and task constraints. DEFT generalizes across failure types, supports constrained and unconstrained motions, and enables task completion under arbitrary failure. We evaluated DEFT in both simulation and real-world scenarios using a 7-DoF robotic arm. In simulation over thousands of joint-failure cases across multiple tasks, DEFT outperformed the baseline by up to 2 times. On failures unseen during training, it continued to outperform the baseline, indicating robust generalization in simulation. Further, we performed real-world evaluations on two multi-step tasks, drawer manipulation and whiteboard erasing. These experiments demonstrated DEFT succeeding on tasks where classical methods failed. Our results show that DEFT achieves fail-active manipulation across arbitrary failure configurations and real-world deployments.

Guohui Ding, Joewie J. Koh, Kelly Merckaert et al.

We consider solving a cooperative multi-robot object manipulation task using reinforcement learning (RL). We propose two distributed multi-agent RL approaches: distributed approximate RL (DA-RL), where each agent applies Q-learning with individual reward functions; and game-theoretic RL (GT-RL), where the agents update their Q-values based on the Nash equilibrium of a bimatrix Q-value game. We validate the proposed approaches in the setting of cooperative object manipulation with two simulated robot arms. Although we focus on a small system of two agents in this paper, both DA-RL and GT-RL apply to general multi-agent systems, and are expected to scale well to large systems.