OpenMutt - 3D Printed Robotic Quadruped

The objective of the OpenMutt project is to build a modular, open-source quadruped as a multidisciplinary research testbed for students and faculty. The design is based on proven models, including the MIT Mini-Cheetah, NYU Open Dynamic Robot, and Bruton’s openDogV3, with modifications to decrease manufacturing time and cost. OpenMutt utilizes 12 brushless motors, each attached to a cycloidal gearbox for actuation. The quarter model has three degrees of freedom, translational and rotational. A remote control will be used for general movement with impedance and PID controllers for torque and joint control. The majority of parts were additively manufactured with Fused Deposition Modeling(FDM) printers using Polylactic Acid(PLA) and Thermoplastic Polyurethane(TPU). A power supply will be used for quarter model testing, while the full model will use an onboard battery with the battery-management system (BMS). Due to the 13:1 gear ratio of the cycloidal gearbox, motors like the ones selected are adaptable to the model. The purpose behind the application of these methods is to ensure a platform that is easy to construct, iterate and learn with

OpenMutt - 3D Printed Robotic Quadruped

Embry-Riddle Aeronautical University is seeking a robotic dog as a research avenue for different biomechanical designs, control systems, and robotic designs for experimentation and study. The quadruped is based on several open-source platforms including James Bruton’s openDogV3, the MIT Mini-Cheetah, and the NYU Open Dynamic Robot Initiative. The implementation of this research will begin with a quarter model, consisting of a singular leg from the hip to the foot. The leg will be mounted on a benchtop test stand that allows for controlled movement and accessible experimentation. The leg will be separate from the full-model quadruped strictly for experimentation and any full-model revisions. The OpenMutt’s quarter model uses 3 Brushless DC Electric Motors (BLDC) attached to 3 cycloidal gearboxes as its main form of actuation. The majority of parts were manufactured using Polylactic Acid (PLA). Some leg testing has already been completed, but a synchronized movement is yet to be completed

RealAnt: An Open-Source Low-Cost Quadruped for Research in Real-World Reinforcement Learning

Current robot platforms available for research are either very expensive or unable to handle the abuse of exploratory controls in reinforcement learning. We develop RealAnt, a minimal low-cost physical version of the popular 'Ant' benchmark used in reinforcement learning. RealAnt costs only $410 in materials and can be assembled in less than an hour. We validate the platform with reinforcement learning experiments and provide baseline results on a set of benchmark tasks. We demonstrate that the TD3 algorithm can learn to walk the RealAnt from less than 45 minutes of experience. We also provide simulator versions of the robot (with the same dimensions, state-action spaces, and delayed noisy observations) in the MuJoCo and PyBullet simulators. We open-source hardware designs, supporting software, and baseline results for ease of reproducibility

Co-Designing Robots by Differentiating Motion Solvers

We present a novel algorithm for the computational co-design of legged robots and dynamic maneuvers. Current state-of-the-art approaches are based on random sampling or concurrent optimization. A few recently proposed methods explore the relationship between the gradient of the optimal motion and robot design. Inspired by these approaches, we propose a bilevel optimization approach that exploits the derivatives of the motion planning sub-problem (the inner level) without simplifying assumptions on its structure. Our approach can quickly optimize the robot's morphology while considering its full dynamics, joint limits and physical constraints such as friction cones. It has a faster convergence rate and greater scalability for larger design problems than state-of-the-art approaches based on sampling methods. It also allows us to handle constraints such as the actuation limits, which are important for co-designing dynamic maneuvers. We demonstrate these capabilities by studying jumping and trotting gaits under different design metrics and verify our results in a physics simulator. For these cases, our algorithm converges in less than a third of the number of iterations needed for sampling approaches, and the computation time scales linearly.Comment: 8 pages, 7 figures, submitted to IROS 202