Squat Motion of a Humanoid Robot Using Three-Particle Model Predictive Control and Whole-Body Control.

Squatting is a fundamental and crucial movement, often employed as a basic test during robot commissioning, and it plays a significant role in some service industries and in cases when robots perform high-dynamic movements like jumping. Therefore, achieving continuous and precise squatting actions is of great importance for the future development of humanoid robots. In this paper, we apply three-particle model predictive control (TP-MPC) combined with weight-based whole-body control (WBC) to a humanoid robot.

Special Issue: Design and Control of a Bio-Inspired Robot.

Bionics, the interdisciplinary field that draws inspiration from nature to design and develop innovative technologies, has paved the way for the creation of "bio-inspired robots" [...

Learning Quadrupedal High-Speed Running on Uneven Terrain.

Reinforcement learning (RL)-based controllers have been applied to the high-speed movement of quadruped robots on uneven terrains. The external disturbances increase as the robot moves faster on such terrains, affecting the stability of the robot. Many existing RL-based methods adopt higher control frequencies to respond quickly to the disturbance, which requires a significant computational cost.

CBMC: A Biomimetic Approach for Control of a 7-Degree of Freedom Robotic Arm.

Many approaches inspired by brain science have been proposed for robotic control, specifically targeting situations where knowledge of the dynamic model is unavailable. This is crucial because dynamic model inaccuracies and variations can occur during the robot's operation. In this paper, inspired by the central nervous system (CNS), we present a CNS-based Biomimetic Motor Control (CBMC) approach consisting of four modules.

Balanced Standing on One Foot of Biped Robot Based on Three-Particle Model Predictive Control.

Balancing is a fundamental task in the motion control of bipedal robots. Compared to two-foot balancing, one-foot balancing introduces new challenges, such as a smaller supporting polygon and control difficulty coming from the kinematic coupling between the center of mass (CoM) and the swinging leg. Although nonlinear model predictive control (NMPC) may solve this problem, it is not feasible to implement it on the actual robot because of its large amount of calculation.

Dynamic Balancing of Humanoid Robot with Proprioceptive Actuation: Systematic Design of Algorithm, Software, and Hardware.

For humanoid robots, maintaining a dynamic balance against uncertain disturbance is crucial, and this function can be achieved by coordinating the whole body to perform multiple tasks simultaneously. Researchers generally accept hierarchical whole-body control (WBC) to address this function. Although experts can build feasible hierarchies using prior knowledge, real-time WBC is still challenging because it often requires a quadratic program with multiple inequality constraints.

A framework for the general design and computation of hybrid neural networks.

There is a growing trend to design hybrid neural networks (HNNs) by combining spiking neural networks and artificial neural networks to leverage the strengths of both. Here, we propose a framework for general design and computation of HNNs by introducing hybrid units (HUs) as a linkage interface. The framework not only integrates key features of these computing paradigms but also decouples them to improve flexibility and efficiency.

Brain-inspired global-local learning incorporated with neuromorphic computing.

There are two principle approaches for learning in artificial intelligence: error-driven global learning and neuroscience-oriented local learning. Integrating them into one network may provide complementary learning capabilities for versatile learning scenarios. At the same time, neuromorphic computing holds great promise, but still needs plenty of useful algorithms and algorithm-hardware co-designs to fully exploit its advantages.

A hybrid and scalable brain-inspired robotic platform.

Recent years have witnessed tremendous progress of intelligent robots brought about by mimicking human intelligence. However, current robots are still far from being able to handle multiple tasks in a dynamic environment as efficiently as humans. To cope with complexity and variability, further progress toward scalability and adaptability are essential for intelligent robots.

A system hierarchy for brain-inspired computing.

Neuromorphic computing draws inspiration from the brain to provide computing technology and architecture with the potential to drive the next wave of computer engineering. Such brain-inspired computing also provides a promising platform for the development of artificial general intelligence. However, unlike conventional computing systems, which have a well established computer hierarchy built around the concept of Turing completeness and the von Neumann architecture, there is currently no generalized system hierarchy or understanding of completeness for brain-inspired computing.

Towards artificial general intelligence with hybrid Tianjic chip architecture.

There are two general approaches to developing artificial general intelligence (AGI): computer-science-oriented and neuroscience-oriented. Because of the fundamental differences in their formulations and coding schemes, these two approaches rely on distinct and incompatible platforms, retarding the development of AGI. A general platform that could support the prevailing computer-science-based artificial neural networks as well as neuroscience-inspired models and algorithms is highly desirable.