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Having a well-rounded fixed leg design for a quadruped inevitably limits performance across diverse tasks, while tunability enables specialization and leads to better performance. This paper introduces a sub-500-gram quadruped robot with a rich leg design space. Made with laminate design and fabrication techniques, its legs have a range of tunable design parameters, including leg length, transmission ratio, and passive parallel and series stiffness. The legs are also straightforward to model, low-cost, and fast to manufacture. We propose methods to span the leg’s feasible design space and construct simulation environments for training a locomotion policy with reinforcement learning to remove the need for manual controller design and tuning. This policy not only works across leg designs but also exploits the unique dynamics of each leg for better locomotion. A curation process is employed to select designs given performance goals, which is more interpretable than optimization and provides insights for design improvements and discoveries of design principles. Thanks to the tight integration of design, fabrication, simulation, and control, our proposed pipeline produces leg designs with performance that aligns with the simulation, while the learned locomotion policy can be used successfully on the real robot. The fast longitudinal running design reaches a maximum speed of 0.7 m/s or 5.4 body lengths per second, and the low cost of transport (COT) design has a COT of 0.3. 

Redesigning and remanufacturing robots are infeasible for resource-constrained environments like space or undersea. This work thus studies how to evaluate and repurpose existing, complementary, quadruped legs for new tasks. We implement this approach on 15 robot designs generated from combining six pre-selected leg designs. The performance maps for force-based locomotion tasks like pulling, pushing, and carrying objects are constructed via a learned policy that works across all designs and adapts to the limits of each. Performance predictions agree well with real-world validation results. The robot can locomote at 0.5 body lengths per second while exerting a force that is almost 60% of its weight. 

Abstract The stiffness of robot legs greatly affects legged locomotion performance; tuning that stiffness, however, can be a costly and complex task. In this paper, we directly tune the stiffness of jumping robot legs using an origami-inspired laminate design and fabrication method. In addition to the stiffness coefficient described by Hooke’s law, the nonlinearity of the force-displacement curve can also be tuned by optimizing the geometry of the mechanism. Our method reduces the number of parts needed to realize legs with different stiffness while simplifying manual redesign effort, lowering the cost of legged robots while speeding up the design and optimization process. We have fabricated and tested the leg across six different stiffness profiles that vary both the nonlinearity and coefficient. Through a vertical jumping experiment actuated by a DC motor, we also show that proper tuning of the leg stiffness can result in an 18% improvement in lift-off speed and an increase of 19% in peak power output. 

To facilitate the study of how passive leg stiffness influences locomotion dynamics and performance, we have developed an affordable and accessible 400 g quadruped robot driven by tunable compliant laminate legs, whose series and parallel stiffness can be easily adjusted; fabrication only takes 2.5 hours for all four legs. The robot can trot at 0.52 m/s or 4.4 body lengths per second with a 3.2 cost of transport (COT). Through locomotion experiments in both the real world and simulation we demonstrate that legs with different stiffness have an obvious impact on the robot’s average speed, COT, and pronking height. When the robot is trotting at 4 Hz in the real world, changing the leg stiffness yields a maximum improvement of 37.1% in speed and 62.0% in COT, showing its great potential for future research on locomotion controller designs and leg stiffness optimizations.