Robot scooping is a challenging and important task in robotic tool manipulation research due to the complex relationship between the robot, the tool, and target objects/environment. Taking into account different tools, different target objects and varying environments, the required scooping manipulation strategy usually varies greatly. Even considering a specific type of spoon, the question of how to obtain a policy model that requires less demonstration data but shows better generalization capabilities deserves further exploration. In this paper, we propose a progressive learning framework for general robot scooping tasks, which requires a limited number of demonstrations but shows promising generalization capability. We first learn a scooping policy via human demonstrations with a specific setup. We then use this as a pre-train model for reinforcement learning in a curriculum manner to achieve a scooping strategy that is generalizable to different task setups. Finally, we evaluate the capabilities of the policy with a series of experiments both in simulation and on a real robot.

Robotic hand tool use has garnered significant attention from robotics researchers, because it enhances dexterity beyond the limitations imposed by manipulators with fixed tool configurations and human-involved manual tool changes. Despite extensive research, current methodologies predominantly focus on imitating human hand trajectories, often neglecting the pivotal role of tool-environment interaction. This study addresses this gap by exploring the task of cucumber peeling as a case study to implement contact-based demonstration strategies in robotic tool use. Our approach concentrates on the subtle tool contact behaviors that manifest through contact dynamics. Specifically, we select appropriate tool stiffness for the peeling tasks, which is captured via a handheld teaching device equipped with optical tactile sensors. Subsequently, object-level stiffness control strategies are employed to emulate these behaviors using a three-fingered robotic hand. Experimental results from real-world cucumber peeling trials substantiate our methodology, illustrating that the robotic hand can adjust contact through finger movements, thereby achieving humanlike peeling efficiency without necessitating alterations to the tool structure. This study not only demonstrates the feasibility of sophisticated tool use by robotic hands, but also highlights the critical importance of integrating tactile feedback to refine interaction with the environment.

Anthropomorphic robotic arms, mimicking the structure and function of human arms, show great potential for helping people in various tedious and repetitive household tasks. However, such arms mostly consist of multiple serial links controlled independently by actuators at joints with high reduction ratios, posing challenges in household services in terms of load capacity, responsiveness, and safety. In this paper, we propose a high-performance anthropomorphic arm called TRX-Arm based on differential cable transmission, characterized by features of high dynamics, high load capacity, and inherent compliance. TRX-Arm is composed of three deferential cable-driven coupling joints and one independent roll joint. Thanks to the cable differential transmission, the joints are capable of achieving doubled torque and stiffness without replacing motors. To enhance safety in human-robot interaction, the actuators including motors, reducer, belt, and pulley are mounted at the shoulder near the base and drive the joints remotely using cables, thereby minimizing the inertia of the whole arm. The workspace of TRX-Arm has a volume of 1.56 m3, much larger than that of the human arm. Real experiments show its capabilities including high repeatability and load capacity as well as high dynamic behavior of a dual-arm robot platform built with TRX-Arms.

To enrich legged robots with fast energy-efficient mobility on even terrain, wheeled-legged robots have emerged as a valued robot form in robotics research. This paper describes the complete development of a new wheeled-legged quadruped robot named Max, ranging from its mechanical design over system architecture to core algorithms implemented for it to realize various motion behaviors. Instead of attaching wheels to the distal ends of legs as in the existing wheeled-legged robot designs, this robot has wheels installed on the knees with a special switching mechanism to convert a leg between the legged and wheeled locomotion modes. This design keeps the wheeled leg lightweight, enabling the robot to preserve the motion agility as a quadruped robot while gaining the energy-efficiency as a four-wheel or even two-wheel mobile robot. An online locomotion generation method is proposed to compute the 6-D body trajectory of the robot in walking on the perceived terrain, while dynamic movements such as leaps and flips are generated by a unified trajectory optimizer, which is also used to generate the transition motions of the robot to transform into the wheeled mode. The diverse mobility of the proposed robot Max is verified with extensive experiments.Note to Practitioners—Empowering robots with all-terrain mobility is a fundamental open problem in developing a new generation of robots. To this end, combinations of wheels and legs have been explored for robots to possess both traversability on uneven terrains and efficiency on even terrains. This paper proposes a new wheeled-legged quadruped robot with focuses on the integrated design of wheeled legs, system architecture, and core algorithms implemented for various legged and wheeled locomotion behaviors. To embed wheels without adding additional motors and keep the light weight of original legs, a special switching mechanism is designed and integrated at the knee joints where wheels are installed. Algorithms for generating quadrupedal walk according to online perceived terrain information as well as other dynamic legged and wheeled motions are discussed and demonstrated. The system architecture for allocating all vision and motion algorithms is also presented. This work is intended to provide a whole picture of developing this new robot including both hardware and software aspects.

Meta-reinforcement learning enables artificial agents to learn from related training tasks and adapt to new tasks efficiently with minimal interaction data. However, most existing research is still limited to narrow task distributions that are parametric and stationary, and does not consider out-of-distribution tasks during the evaluation, thus, restricting its application. In this paper, we propose MoSS, a context-based Meta-reinforcement learning algorithm based on Self-Supervised task representation learning to address this challenge. We extend meta-RL to broad non-parametric task distributions which have never been explored before, and also achieve state-of-the-art results in non-stationary and out-of-distribution tasks. Specifically, MoSS consists of a task inference module and a policy module. We utilize the Gaussian mixture model for task representation to imitate the parametric and non-parametric task variations. Additionally, our online adaptation strategy enables the agent to react at the first sight of a task change, thus being applicable in non-stationary tasks. MoSS also exhibits strong generalization robustness in out-of-distributions tasks which benefits from the reliable and robust task representation. The policy is built on top of an off-policy RL algorithm and the entire network is trained completely off-policy to ensure high sample efficiency. On MuJoCo and Meta-World benchmarks, MoSS outperforms prior works in terms of asymptotic performance, sample efficiency (3-50x faster), adaptation efficiency, and generalization robustness on broad and diverse task distributions.

The wheel-bipedal robot has the advantages of both wheeled robots and legged robots, but as a cost, it is more challenging to perform flexible movements in various surroundings while keeping it balanced. The inaccurate dynamics of the robot makes the balance problem even more intractable. To solve this problem, the robot Ollie is used as a testbed. The whole-body control (WBC) framework is adopted to enhance the dexterity of the robot with multiple degrees of freedom in the task space. Moreover, a learning-based adaptive technique is applied to assist the WBC such that the balance controller can be designed in the absence of the accurate dynamics. Physical experiments demonstrate that the robot can manage various actions, with the help of the combination of the WBC and the learning-based adaptive technique.

This paper presents a balance control technique for a novel wheel-legged robot. We first derive a dynamic model of the robot and then apply a linear feedback controller based on output regulation and linear quadratic regulator (LQR) methods to maintain the standing of the robot on the ground without moving backward and forward mightily. To take into account nonlinearities of the model and obtain a large domain of stability, a nonlinear controller based on the interconnection and damping assignment - passivity-based control (IDA-PBC) method is exploited to control the robot in more general scenarios. Physical experiments are performed with various control tasks. Experimental results demonstrate that the proposed linear output regulator can maintain the standing of the robot, while the proposed nonlinear controller can balance the robot under an initial starting angle far away from the equilibrium point, or under a changing robot height.

In this paper, nonlinear control techniques are exploited to balance an unmanned bicycle with enlarged stability domain. We consider two cases. For the first case when the autonomous bicycle is balanced by the flywheel, the steering angle is set to zero, and the torque of the flywheel is used as the control input. The controller is designed based on the Interconnection and Damping Assignment Passivity Based Control (IDA-PBC) method. For the second case when the bicycle is balanced by the handlebar, the bicycle's velocity is high, and the flywheel is turned off. The angular velocity of the handlebar is used as the control input and the balance controller is designed based on feedback linearization. In these cases, the global stability of the closed-loop unmanned bicycle is theoretically proved based on Lyapunov theory. The experiments are conducted to validate the efficacy of the proposed nonlinear balance controllers.

In this paper, the gain scheduling technique is applied to design a balance controller for an autonomous bicycle with an inertia wheel. Previously, two different balance controllers are needed depending on whether the bicycle is stationary or dynamic. The switch between the two different controllers may cause the instability of the autonomous bicycle. Our proposed gain scheduled controller can balance the autonomous bicycle in both stationary and dynamic cases. A physical system is built and experiments are carried out to demonstrate the effectiveness of the gain scheduled controller.