In both animals and robots, locomotion capabilities are determined by the physical structure of the system. The majority of legged animals and robots are bilaterally symmetric, which facilitates locomotion with consistent headings and obstacle traversal, but leads to constraints in their turning ability. On the other hand, radially symmetric animals have demonstrated rapid turning abilities enabled by their omnidirectional body plans. Radially symmetric tripedal robots are able to turn instantaneously, but are commonly constrained by needing to change direction with every step, resulting in inefficient and less stable locomotion. Inspired by the radial symmetry and maneuverability of brittle stars and octopuses, we introduce a novel design for a tripedal robot that has both frictional and rolling contacts. Additionally, a freely rotating central sphere provides an added contact point so the robot can retain a stable tripod base of support while lifting and pushing with any one of its legs. The SKating, OmniOriented, Tripedal Robot (SKOOTR) is more versatile and stable than existing tripedal robots. It is capable of multiple forward gaits, multiple turning maneuvers, obstacle traversal, and stair climbing. SKOOTR has been designed to facilitate customization for diverse applications: it is fully open-source, is constructed with 3D printed or off-the-shelf parts, and costs approximately $ 500 USD to build. A project page with CAD files, assembly guide, and links to the github repository is posted at https://www.embirlab.com/skootr.

Neural Radiance Fields and Gaussian Splatting have recently transformed computer vision by enabling photo-realistic representations of complex scenes. However, they have seen limited application in real-world robotics tasks such as trajectory optimization. This is due to the difficulty in reasoning about collisions in radiance models and the computational complexity associated with operating in dense models. This paper addresses these challenges by proposing SPLANNING, a risk-aware trajectory optimizer operating in a Gaussian Splatting model. This paper first derives a method to rigorously upper-bound the probability of collision between a robot and a radiance field. Then, this paper introduces a normalized reformulation of Gaussian Splatting that enables efficient computation of this collision bound. Finally, this paper presents a method to optimize trajectories that avoid collisions in a Gaussian Splat. Experiments show that SPLANNING outperforms state-of-the-art methods in generating collision-free trajectories in cluttered environments. The proposed system is also tested on a real-world robot manipulator. A project page is available athttps://roahmlab.github.io/splanning.

Impressive animal locomotion capabilities are mediated by the co-evolution of the skeletal morphology and muscular properties. Legged robot performance would also likely benefit from the co-optimization of actuators and leg morphology. However, development of custom actuators for legged robots is expensive and time consuming, discouraging application-specific actuator optimization. This paper presents open-source designs for two quasi-direct-drive actuators with performance regimes appropriate for an 8–15 kg robot, built from off the shelf and 3D-printed components for less than $200 USD each. The mechanical, electrical, and thermal properties of each actuator are characterized and compared to benchmark data. Actuators subjected to 420k strides of gait data experienced only a 2% reduction in efficiency and 26 mrad in backlash growth, demonstrating viability for rigorous and sustained research applications. We present a thermal solution that nearly doubles the thermally-driven torque limits of our plastic actuator design. The performance results are comparable to traditional metallic actuators for use in high-speed legged robots of the same scale. These 3D printed designs demonstrate an approach for designing and characterizing low-cost, highly customizable and reproducible actuators, democratizing the field of actuator design and enabling co-design and optimization of actuators and robot legs.