Alireza Ramezani

Paul Ghanem, Ahmet Demirkaya, Tales Imbiriba et al.

Learning dynamics governing physical and spatiotemporal processes is a challenging problem, especially in scenarios where states are partially measured. In this work, we tackle the problem of learning dynamics governing these systems when parts of the system's states are not measured, specifically when the dynamics generating the non-measured states are unknown. Inspired by state estimation theory and Physics Informed Neural ODEs, we present a sequential optimization framework in which dynamics governing unmeasured processes can be learned. We demonstrate the performance of the proposed approach leveraging numerical simulations and a real dataset extracted from an electro-mechanical positioning system. We show how the underlying equations fit into our formalism and demonstrate the improved performance of the proposed method when compared with baselines.

Vincent Gherold, Ioannis Mandralis, Eric Sihite et al.

Autonomous robots operating in real environments are often faced with decisions on how best to navigate their surroundings. In this work, we address a particular instance of this problem: how can a robot autonomously decide on the energetically optimal path to follow given a high-level objective and information about the surroundings? To tackle this problem we developed a self-supervised learning method that allows the robot to estimate the cost of transport of its surroundings using only vision inputs. We apply our method to the multi-modal mobility morphobot (M4), a robot that can drive, fly, segway, and crawl through its environment. By deploying our system in the real world, we show that our method accurately assigns different cost of transports to various types of environments e.g. grass vs smooth road. We also highlight the low computational cost of our method, which is deployed on an Nvidia Jetson Orin Nano robotic compute unit. We believe that this work will allow multi-modal robotic platforms to unlock their full potential for navigation and exploration tasks.

Paul Ghanem, Owen Howell, Michael Potter et al.

Inferring an adversary's goals from exhibited behavior is crucial for counterplanning and non-cooperative multi-agent systems in domains like cybersecurity, military, and strategy games. Deep Inverse Reinforcement Learning (IRL) methods based on maximum entropy principles show promise in recovering adversaries' goals but are typically offline, require large batch sizes with gradient descent, and rely on first-order updates, limiting their applicability in real-time scenarios. We propose an online Recursive Deep Inverse Reinforcement Learning (RDIRL) approach to recover the cost function governing the adversary actions and goals. Specifically, we minimize an upper bound on the standard Guided Cost Learning (GCL) objective using sequential second-order Newton updates, akin to the Extended Kalman Filter (EKF), leading to a fast (in terms of convergence) learning algorithm. We demonstrate that RDIRL is able to recover cost and reward functions of expert agents in standard and adversarial benchmark tasks. Experiments on benchmark tasks show that our proposed approach outperforms several leading IRL algorithms.

Eric Sihite, Pravin Dangol, Alireza Ramezani

We are seeking control design paradigms for legged systems that allow bypassing costly algorithms that depend on heavy on-board computers widely used in these systems and yet being able to match what they can do by using less expensive optimization-free frameworks. In this work, we present our preliminary results in modeling and control design of a quadrupedal robot called \textit{Husky Carbon}, which under development at Northeastern University (NU) in Boston. In our approach, we utilized a supervisory controller and an Explicit Reference Governor (ERG) to enforce ground reaction force constraints. These constraints are usually enforced using costly optimizations. However, in this work, the ERG manipulates the state references applied to the supervisory controller to enforce the ground contact constraints through an updated law based on Lyapunov stability arguments. As a result, the approach is much faster to compute than the widely used optimization-based methods.

Paul Ghanem, Yunus Bicer, Deniz Erdogmus et al.

Fluidic locomotion of flapping Micro Aerial Vehicles (MAVs) can be very complex, particularly when the rules from insect flight dynamics (fast flapping dynamics and light wings) are not applicable. In these situations, widely used averaging techniques can fail quickly. The primary motivation is to find efficient models for complex forms of aerial locomotion where wings constitute a large part of body mass (i.e., dominant inertial effects) and deform in multiple directions (i.e., morphing wing). In these systems, high degrees of freedom yields complex inertial, Coriolis, and gravity terms. We use Algorithmic Differentiation (AD) and Bayesian filters computed with cubature rules conjointly to quickly estimate complex fluid-structure interactions. In general, Bayesian filters involve finding complex numerical integration (e.g., find posterior integrals). Using cubature rules to compute Gaussian-weighted integrals and AD, we show that the complex multi-degrees-of-freedom dynamics of morphing MAVs can be computed very efficiently and accurately. Therefore, our work facilitates closed-loop feedback control of these morphing MAVs.

Eric Sihite, Pravin Dangol, Alireza Ramezani

In this paper, we study the regulation of the Ground Contact Forces (GRF) in thruster-assisted legged locomotion. We will employ Reference Governors (RGs) for enforcing GRF constraints in Harpy model which is a bipedal robot that is being developed at Northeastern University. Optimization-based methods and whole body control are widely used for enforcing the no-slip constraints in legged locomotion which can be very computationally expensive. In contrast, RGs can enforce these constraints by manipulating joint reference trajectories using Lyapunov stability arguments which can be computed much faster. The addition of the thrusters in our model allows to manipulate the gait parameters and the GRF without sacrificing the locomotion stability.

Pravin Dangol, Alireza Ramezani

Real-time constraint satisfaction for robots can be quite challenging due to the high computational complexity that arises when accounting for the system dynamics and environmental interactions, often requiring simplification in modelling that might not necessarily account for all performance criteria. We instead propose an optimization-free approach where reference trajectories are manipulated to satisfy constraints brought on by ground contact as well as those prescribed for states and inputs. Unintended changes to trajectories especially ones optimized to produce periodic gaits can adversely affect gait stability, however we will show our approach can still guarantee stability of a gait by employing the use of coaxial thrusters that are unique to our robot.

Alireza Ramezani, Pravin Dangol, Eric Sihite et al.

We report the design of a morpho-functional robot called Husky Carbon. Our goal is to integrate two forms of mobility, aerial and quadrupedal-legged locomotion, within a single platform. There are prohibitive design restrictions such as tight power budget and payload, which can particularly become important in aerial flights. To address these challenges, we pose a problem called the Mobility Value of Added Mass (MVAM) problem. In the MVAM problem, we attempt to allocate mass in our designs such that the energetic performance is affected the least. To solve the MVAM problem, we adopted a generative design approach using Grasshopper's evolutionary solver to synthesize a parametric design space for Husky. Then, this space was searched for the morphologies that could yield a minimized Total Cost Of Transport (TCOT) and payload. This approach revealed that a front-heavy quadrupedal robot can achieve a lower TCOT while retaining larger margins on allowable added mass to its design. Based on this framework Husky was built and tested as a front-heavy robot.

Kaier Liang, Eric Sihite, Pravin Dangol et al.

Despite many accomplishments by legged robot designers, state-of-the-art bipedal robots are prone to falling over, cannot negotiate extremely rough terrains and cannot directly regulate unilateral contact forces. Our objective is to integrate merits of legged and aerial robots in a single platform. We will show that the thrusters in a bipedal legged robot called Harpy can be leveraged to stabilize the robot's frontal dynamics and permit jumping over large obstacles which is an unusual capability not reported before. In addition, we will capitalize on the thrusters action in Harpy and will show that one can avoid using costly optimization-based schemes by directly regulating contact forces using an Reference Governor (RGs). We will resolve gait parameters and re-plan them during gait cycles by only assuming well-tuned supervisory controllers. Then, we will focus on RG-based fine-tuning of the joints desired trajectories to satisfy unilateral contact force constraints.

Eric Sihite, Atefe Darabi, Pravin Dangol et al.

Our goal in this work is to expand the theory and practice of robot locomotion by addressing critical challenges associated with the robotic biomimicry of bat aerial locomotion. Bats are known for their pronounced, fast wing articulations, e.g., bats can mobilize as many as forty joints during a single wingbeat, with some joints reaching over one thousand degrees per second in angular speed. Copying bats flight is a significant ordeal, however, very rewarding. Aerial drones with morphing bodies similar to bats can be safer, agile and energy-efficient owing to their articulated and soft wings. Current design paradigms have failed to copy bat flight because they assume only closed-loop feedback roles and ignore computational roles carried out by morphology. To respond to the urgency, a design framework called Morphing via Integrated Mechanical Intelligence and Control (MIMIC) is proposed. In this paper, using the dynamic model of Northeastern University's Aerobat, which is designed to test the effectiveness of the MIMIC framework, it will be shown that computational structures and closed-loop feedback can be successfully used to mimic bats stable flight apparatus.

Andrew Lessieur, Eric Sihite, Pravin Dangol et al.

Biologically-inspired robots are a very interesting and difficult branch of robotics dues to its very rich dynamical and morphological complexities. Among them, flying animals, such as bats, have been among the most difficult to take inspiration from as they exhibit complex wing articulation. We attempt to capture several of the key degrees-of-freedom that are present in the natural flapping gait of a bat. In this work, we present the mechanical design and analysis of our flapping wing robot, the Aerobat, where we capture the plunging and flexion-extension in the bat's flapping modes. This robot utilizes gears, cranks, and four-bar linkage mechanisms to actuate the arm-wing structure composed of rigid and flexible components monolithically fabricated using PolyJet 3D printing. The resulting robot exhibits wing expansion and retraction during the downstroke and upstroke respectively which minimizes the negative lift and results in a more efficient flapping gait.

Eric Sihite, Andrew Lessieur, Pravin Dangol et al.

Our goal in this work is to expand the theory and practice of robot locomotion by addressing critical challenges associated with the robotic biomimicry of bat aerial locomotion. Bats wings exhibit fast wing articulation and can mobilize as many as 40 joints within a single wingbeat. Mimicking bat flight can be a significant ordeal and the current design paradigms have failed as they assume only closed-loop feedback roles through sensors and conventional actuators while ignoring the computational role carried by morphology. In this paper, we propose a design framework called Morphing via Integrated Mechanical Intelligence and Control (MIMIC) which integrates small and low energy actuators to control the robot through a change in morphology. In this paper, using the dynamic model of Northeastern University's Aerobat, which is designed to test the effectiveness of the MIMIC framework, it will be shown that computational structures and closed-loop feedback can be successfully used to mimic bats stable flight apparatus.

Peyman Tahghighi, Reza A. Zoroofi, Sare Safi et al.

For medical diagnosis based on retinal images, a clear understanding of 3D structure is often required but due to the 2D nature of images captured, we cannot infer that information. However, by utilizing 3D reconstruction methods, we can recover the height information of the macula area on a fundus image which can be helpful for diagnosis and screening of macular disorders. Recent approaches have used shading information for heightmap prediction but their output was not accurate since they ignored the dependency between nearby pixels and only utilized shading information. Additionally, other methods were dependent on the availability of more than one image of the retina which is not available in practice. In this paper, motivated by the success of Conditional Generative Adversarial Networks(cGANs) and deeply supervised networks, we propose a novel architecture for the generator which enhances the details and the quality of output by progressive refinement and the use of deep supervision to reconstruct the height information of macula on a color fundus image. Comparisons on our own dataset illustrate that the proposed method outperforms all of the state-of-the-art methods in image translation and medical image translation on this particular task. Additionally, perceptual studies also indicate that the proposed method can provide additional information for ophthalmologists for diagnosis.

Eric Sihite, Alireza Ramezani

Flapping wing flight is a challenging dynamical problem and is also a very fascinating subject to study in the field of biomimetic robotics. A Bat, in particular, has a very articulated armwing mechanism with high degrees-of-freedom and flexibility which allows the animal to perform highly dynamic and complex maneuvers, such as upside-down perching. This paper presents the derivation of a multi-body dynamical system of a bio-inspired bat robot called Aerobat which captures multiple biologically meaningful degrees-of-freedom for flapping flight that is present in biological bats. Then, the work attempts to manifest closed-loop aerial body reorientation and preparation for landing through the manipulation of inertial dynamics and aerodynamics by enforcing nonholonomic constraints onto the system. The proposed design paradigm assumes for rapidly exponentially stable controllers that enforce holonomic constraints in the joint space of the model. A model and optimization-based nonlinear controller is applied to resolve the joint trajectories such that the desired angular momentum about the roll axis is achieved.

Peyman Tahghighi, Reza A. Zoroofi, Sare Safi et al.

For screening, 3D shape of the eye retina often provides structural information and can assist ophthalmologists to diagnose diseases. However, fundus images which are one the most common screening modalities for retina diagnosis lack this information due to their 2D nature. Hence, in this work, we try to infer about this 3D information or more specifically its heights. Recent approaches have used shading information for reconstructing the heights but their output is not accurate since the utilized information is not sufficient. Additionally, other methods were dependent on the availability of more than one image of the eye which is not available in practice. In this paper, motivated by the success of Conditional Generative Adversarial Networks(cGANs) and deeply supervised networks, we propose a novel architecture for the generator which enhances the details in a sequence of steps. Comparisons on our dataset illustrate that the proposed method outperforms all of the state-of-the-art methods in image translation and medical image translation on this particular task. Additionally, clinical studies also indicate that the proposed method can provide additional information for ophthalmologists for diagnosis.

Arthur C. B. de Oliveira, Alireza Ramezani

This work tries to contribute to the design of legged robots with capabilities boosted through thruster-assisted locomotion. Our long-term goal is the development of robots capable of negotiating unstructured environments, including land and air, by leveraging legs and thrusters collaboratively. These robots could be used in a broad number of applications including search and rescue operations, space exploration, automated package handling in residential spaces and digital agriculture, to name a few. In all of these examples, the unique capability of thruster-assisted mobility greatly broadens the locomotion designs possibilities for these systems. In an effort to demonstrate thrusters effectiveness in the robustification and efficiency of bipedal locomotion gaits, this work explores their effects on the gait limit cycles and proposes new design paradigms based on shaping these center manifolds with strong foliations. Unilateral contact force feasibility conditions are resolved in an optimal control scheme.

Alireza Ramezani

The flight characteristics of bats remarkably have been overlooked in aerial drone designs. Unlike other animals, bats leverage the manipulation of inertial dynamics to exhibit aerial flip turns when they perch. Inspired by this unique maneuver, this work develops and uses a tiny robot called \textit{Harpoon} to demonstrate that the preparation for upside-down landing is possible through: 1) reorientation towards the landing surface through zero-angular-momentum turns and 2) reaching to the surface through shooting a detachable landing gear. The closed-loop manipulations of inertial dynamics takes place based on a symplectic description of the dynamical system (body and appendage), which is known to exhibit an excellent geometric conservation properties.

Pravin Dangol, Alireza Ramezani

In this paper, we will report our efforts in designing closed-loop feedback for the thruster-assisted walking of bipedal robots. We will assume for well-tuned supervisory controllers and will focus on fine-tuning the joints desired trajectories to satisfy the performance being sought. In doing this, we will devise an intermediary filter based on reference governors that guarantees the satisfaction of performance-related constraints. Since these modifications and impact events lead to deviations from the desired periodic orbits, we will guarantee hybrid invariance in a robust way by applying predictive schemes withing a very short time envelope during the gait cycle. To achieve the hybrid invariance, we will leverage the unique features in our model, that is, the thrusters. The merit of our approach is that unlike existing optimization-based nonlinear control methods, satisfying performance-related constraints during the single support phase does not rely on expensive numeric approaches. In addition, the overall structure of the proposed thruster-assisted gait control allows for exploiting performance and robustness enhancing capabilities during specific parts of the gait cycle, which is unusual and not reported before.

Pravin Dangol, Alireza Ramezani, Nader Jalili

We will report our efforts in designing feedback for the thruster-assisted walking of a bipedal robot. We will assume for well-tuned supervisory controllers and will focus on fine-tuning the desired joint trajectories to satisfy the performance being sought. In doing this, we will devise an intermediary filter based on the emerging idea of reference governors. Since these modifications and impact events lead to deviations from the desired periodic orbits, we will guarantee hybrid invariance in a robust fashion by applying predictive schemes within a short time envelope during the double support phase of a gait cycle. To achieve the hybrid invariance, we will leverage the unique features in our robot, i.e., the thruster.