Legged robots have the potential to become vital in maintenance, home support, and exploration scenarios. In order to interact with and manipulate their environments, most legged robots are equipped with a dedicated robot arm, which means additional mass and mechanical complexity compared to standard legged robots. In this work, we explore pedipulation - using the legs of a legged robot for manipulation. By training a reinforcement learning policy that tracks position targets for one foot, we enable a dedicated pedipulation controller that is robust to disturbances, has a large workspace through whole-body behaviors, and can reach far-away targets with gait emergence, enabling loco-pedipulation. By deploying our controller on a quadrupedal robot using teleoperation, we demonstrate various real-world tasks such as door opening, sample collection, and pushing obstacles. We demonstrate load carrying of more than 2.0 kg at the foot. Additionally, the controller is robust to interaction forces at the foot, disturbances at the base, and slippery contact surfaces. Videos of the experiments are available at https://sites.google.com/leggedrobotics.com/pedipulate.

We present SpaceHopper, a three-legged, small-scale robot designed for future mobile exploration of asteroids and moons. The robot weighs 5.2 kg and has a body size of 245 mm while using space-qualifiable components. Furthermore, SpaceHopper’s design and controls make it well-adapted for investigating dynamic locomotion modes with extended flight-phases. Instead of gyroscopes or fly-wheels, the system uses its three legs to reorient the body during flight in preparation for landing. We control the leg motion for reorientation using Deep Reinforcement Learning policies. In a simulation of Ceres’ gravity (0.029 g), the robot can reliably jump to commanded positions up to 6 m away. Our real-world experiments show that SpaceHopper can successfully reorient to a safe landing orientation within 9.7 deg inside a rotational gimbal and jump in a counterweight setup in Earth’s gravity. Overall, we consider SpaceHopper an important step towards controlled jumping locomotion in low-gravity environments.

Accurate simulation of soft mechanisms under dynamic actuation is critical for the design of soft robots. We address this gap with our differentiable simulation tool by learning the material parameters of our soft robotic fish. On the example of a soft robotic fish, we demonstrate an experimentally-verified, fast optimization pipeline for learning the material parameters from quasi-static data via differentiable simulation and apply it to the prediction of dynamic performance. Our method identifies physically plausible Young's moduli for various soft silicone elastomers and stiff acetal copolymers used in creation of our three different robotic fish tail designs. We show that our method is compatible with varying internal geometry of the actuators, such as the number of hollow cavities. Our framework allows high fidelity prediction of dynamic behavior for composite bi-morph bending structures in real hardware to millimeter-accuracy and within 3% error normalized to actuator length. We provide a differentiable and robust estimate of the thrust force using a neural network thrust predictor; this estimate allows for accurate modeling of our experimental setup measuring bollard pull. This work presents a prototypical hardware and simulation problem solved using our differentiable framework; the framework can be applied to higher dimensional parameter inference, learning control policies, and computational design due to its differentiable character.

This paper introduces SpaceBok, a quadrupedal robot created to investigate dynamic legged locomotion for the exploration of low-gravity celestial bodies. With a hip height of 500 mm and a mass of 20 kg, its dimensions are comparable to a medium-sized dog. The robot's leg configuration is based on an optimized parallel motion mechanism that allows the integration of parallel elastic elements to store and release energy for powerful jumping maneuvers. High-torque brushless motors in combination with customized single-stage planetary gear transmissions enable force control at the foot contact points based on motor currents. We present successful walking, trotting, and pronking experiments. Thereby, Spacebok achieved maximal jump heights in single jump experiments of up to 1.05 m (more than twice the hip height) and a walking velocity of 1m/s. Moreover, simulation results for low gravity on the moon suggest that our robot can move with up to 1.1m/s at an approximate cost of transport of 1 in moon gravity when using the pronking gait.