The autonomous transportation of materials over challenging terrain is a challenge with major economic implications and remains unsolved. This paper introduces LEVA, a high-payload, high-mobility robot designed for autonomous logistics across varied terrains, including those typical in agriculture, construction, and search and rescue operations. LEVA uniquely integrates an advanced legged suspension system using parallel kinematics. It is capable of traversing stairs using a reinforcement learning (RL) controller, has steerable wheels, and includes a specialized box pickup mechanism that enables autonomous payload loading as well as precise and reliable cargo transportation of up to 85 kg across uneven surfaces, steps and inclines while maintaining a Cost of Transportation (CoT) of as low as 0.15. Through extensive experimental validation, LEVA demonstrates its off-road capabilities and reliability regarding payload loading and transport.

Power consumption is a frequently over-looked aspect in robotics, especially in the context of legged robots. Nevertheless, improving the efficiency of walking robots is crucial to overcome the current limitations in runtime. This work proposes a novel method for precisely estimating actuator power consumption based on LSTM neural networks. The performance of this approach is benchmarked against currently employed models and validated on real hardware using certified instruments. The proposed method is integrated into the Isaac Gym framework and utilized to train a power-efficient policy. Instead of optimizing for handcrafted cost functions, such as the often used torque-square minimization, our approach for the first time trains RL policies that minimize the effective energy consumption. Hardware results demonstrate a reduction of approximately 25% in the robot’s total power consumption, with a notable 50% decrease observed for the knee actuator. Additionally, the newly developed policy generates significantly smoother and quieter motions.

Scientific exploration of planetary bodies is an activity well-suited for robots. Unfortunately, the regions that are richer in potential discoveries, such as impact craters, caves, and volcanic terraces, are hard to access with wheeled robots. Recent advances in legged-based approaches have shown the potential of the technology to overcome difficult terrains such as slopes and slippery surfaces. In this work, we focus on locomotion for sandy slopes, comparing standard walking policies with a novel crawling-based gait for quadrupedal robots. We fine-tuned a state-of-the-art locomotion framework and introduced hardware modifications to the robot ANYmal, which enables walking on its knees. Moreover, we integrated a novel metric for stability, the stability margin, in the training process to increase robustness in such conditions. We benchmarked the locomotion policies in simulation and in real-world experiments on a martian soil simulant. Our results show a significant improvement in terms of robustness and stability, especially at higher slope angles beyond 15 degrees.

The vast majority of state-of-the-art walking robots employ flat or ball feet for locomotion, presenting limitations while stepping on obstacles, slopes, or unstructured terrain. Moreover, traditional feet for quadrupeds lack sensing systems that are able to provide information about the environment and about the foot interaction with the surroundings. This further diminishes their value. Inspired by our previous work on soft feet for bipedal robots, we present the SoftFoot-Q, an articulated adaptive foot for quadrupeds. This device is conceived to be robust and able to overcome the limitations of currently employed feet. The core idea behind our adaptive foot design is first introduced and validated through a simplified mathematical formulation of the problem. Subsequently, we present the chosen mechanical implementation to attempt overcoming current limitations. The realized prototype of adaptive foot is integrated and tested on the compliantly actuated quadrupedal robot ANYmal together with an ROS-based real-time foot pose reconstruction software. Both extensive field tests and indoor experiments show noticeable performance improvements, in terms of reduced slippage of the robot, with respect to both flat and ball feet.