Robust Recovery Motion Control for Quadrupedal Robots via Learned Terrain Imagination

Quadrupedal robots have emerged as a cutting-edge platform for assisting humans, finding applications in tasks related to inspection and exploration in remote areas. Nevertheless, their floating base structure renders them susceptible to fall in cluttered environments, where manual recovery by a human operator may not always be feasible. Several recent studies have presented recovery controllers employing deep reinforcement learning algorithms. However, these controllers are not specifically designed to operate effectively in cluttered environments, such as stairs and slopes, which restricts their applicability. In this study, we propose a robust all-terrain recovery policy to facilitate rapid and secure recovery in cluttered environments. We substantiate the superiority of our proposed approach through simulations and real-world tests encompassing various terrain types.

DreamWaQ: Learning Robust Quadrupedal Locomotion With Implicit Terrain Imagination via Deep Reinforcement Learning

Quadrupedal robots resemble the physical ability of legged animals to walk through unstructured terrains. However, designing a controller for quadrupedal robots poses a significant challenge due to their functional complexity and requires adaptation to various terrains. Recently, deep reinforcement learning, inspired by how legged animals learn to walk from their experiences, has been utilized to synthesize natural quadrupedal locomotion. However, state-of-the-art methods strongly depend on a complex and reliable sensing framework. Furthermore, prior works that rely only on proprioception have shown a limited demonstration for overcoming challenging terrains, especially for a long distance. This work proposes a novel quadrupedal locomotion learning framework that allows quadrupedal robots to walk through challenging terrains, even with limited sensing modalities. The proposed framework was validated in real-world outdoor environments with varying conditions within a single run for a long distance.

Retro-RL: Reinforcing Nominal Controller With Deep Reinforcement Learning for Tilting-Rotor Drones

Studies that broaden drone applications into complex tasks require a stable control framework. Recently, deep reinforcement learning (RL) algorithms have been exploited in many studies for robot control to accomplish complex tasks. Unfortunately, deep RL algorithms might not be suitable for being deployed directly into a real-world robot platform due to the difficulty in interpreting the learned policy and lack of stability guarantee, especially for a complex task such as a wall-climbing drone. This paper proposes a novel hybrid architecture that reinforces a nominal controller with a robust policy learned using a model-free deep RL algorithm. The proposed architecture employs an uncertainty-aware control mixer to preserve guaranteed stability of a nominal controller while using the extended robust performance of the learned policy. The policy is trained in a simulated environment with thousands of domain randomizations to achieve robust performance over diverse uncertainties. The performance of the proposed method was verified through real-world experiments and then compared with a conventional controller and the state-of-the-art learning-based controller trained with a vanilla deep RL algorithm.

STEP: State Estimator for Legged Robots Using a Preintegrated foot Velocity Factor

We propose a novel state estimator for legged robots, STEP, achieved through a novel preintegrated foot velocity factor. In the preintegrated foot velocity factor, the usual non-slip assumption is not adopted. Instead, the end effector velocity becomes observable by exploiting the body speed obtained from a stereo camera. In other words, the preintegrated end effector's pose can be estimated. Another advantage of our approach is that it eliminates the necessity for a contact detection step, unlike the typical approaches. The proposed method has also been validated in harsh-environment simulations and real-world experiments containing uneven or slippery terrains.

WALK-VIO: Walking-motion-Adaptive Leg Kinematic Constraint Visual-Inertial Odometry for Quadruped Robots

In this paper, WALK-VIO, a novel visual-inertial odometry (VIO) with walking-motion-adaptive leg kinematic constraints that change with body motion for localization of quadruped robots, is proposed. Quadruped robots primarily use VIO because they require fast localization for control and path planning. However, since quadruped robots are mainly used outdoors, extraneous features extracted from the sky or ground cause tracking failures. In addition, the quadruped robots' walking motion cause wobbling, which lowers the localization accuracy due to the camera and inertial measurement unit (IMU). To overcome these limitations, many researchers use VIO with leg kinematic constraints. However, since the quadruped robot's walking motion varies according to the controller, gait, quadruped robots' velocity, and so on, these factors should be considered in the process of adding leg kinematic constraints. We propose VIO that can be used regardless of walking motion by adjusting the leg kinematic constraint factor. In order to evaluate WALK-VIO, we create and publish datasets of quadruped robots that move with various types of walking motion in a simulation environment. In addition, we verified the validity of WALK-VIO through comparison with current state-of-the-art algorithms.