The field of robotics and control is witnessing significant advancements, driven by innovative research in areas such as modular robot control, rigid body networks, and robust humanoid push recovery. A notable trend is the increasing focus on developing control frameworks that can effectively handle complex tasks, such as navigation, manipulation, and locomotion, in a wide range of environments. Researchers are exploring the use of novel control structures, such as excitable systems and decentralized control methods, to address the challenges posed by non-linear systems and uncertain environments. The development of new metrics and analysis tools is also enabling a better understanding of the interplay between morphological evolution and learning in embodied AI systems. Furthermore, the application of multi-objective optimization techniques and machine learning algorithms is leading to improved performance and adaptability in robotic systems. Noteworthy papers in this area include: Modular Robot Control with Motor Primitives, which introduces a comprehensive framework for modular robot control using motor primitives. Unconventional Hexacopters via Evolution and Learning, which demonstrates the potential of combining evolution and learning to deliver non-conventional drones that outperform traditional designs. Duawlfin: A Drone with Unified Actuation for Wheeled Locomotion and Flight Operation, which presents a novel drone design that achieves efficient ground mobility without the need for additional actuators or propeller-driven ground propulsion.
Advancements in Robotics and Control
Sources
Modular Robot Control with Motor Primitives
Mesh Stability Guaranteed Rigid Body Networks Using Control and Topology Co-Design
Event disturbance rejection: a case study
Bracing for Impact: Robust Humanoid Push Recovery and Locomotion with Reduced Order Models
Benchmarking MOEAs for solving continuous multi-objective RL problems
Weak Pareto Boundary: The Achilles' Heel of Evolutionary Multi-Objective Optimization
From Structural Design to Dynamics Modeling: Control-Oriented Development of a 3-RRR Parallel Ankle Rehabilitation Robot
Duawlfin: A Drone with Unified Actuation for Wheeled Locomotion and Flight Operation
Unconventional Hexacopters via Evolution and Learning: Performance Gains and New Insights
Functional Controllability, Functional Stabilisability, and the Generalised Separation Principle
MMD-Newton Method for Multi-objective Optimization
Fast and scalable multi-robot deployment planning under connectivity constraints
Shape-Adaptive Planning and Control for a Deformable Quadrotor
Toward Task Capable Active Matter: Learning to Avoid Clogging in Confined Collectives via Collisions
Fault-Tolerant Multi-Robot Coordination with Limited Sensing within Confined Environments
Event-based Reconfiguration Control for Time-varying Formation of Robot Swarms in Narrow Spaces
Partitioning and Observability in Linear Systems via Submodular Optimization
Robust Look-ahead Pursuit Control for Three-Dimensional Path Following within Finite-Time Stability Guarantee
SpineWave: Harnessing Fish Rigid-Flexible Spinal Kinematics for Enhancing Biomimetic Robotic Locomotion
Unified Multi-Rate Model Predictive Control for a Jet-Powered Humanoid Robot
Delayed dynamic-feedback controller design for multi-frequency vibration suppression