The field of robotics is moving towards more efficient, high-performance, and adaptive control systems. Recent developments have focused on combining different approaches, such as population-coded spiking neural networks and deep reinforcement learning, to achieve better results in robotic manipulation tasks. Another trend is the use of hierarchical planning and control strategies to enable robots to perform complex tasks in a more efficient and robust way. Noteworthy papers in this area include Population-Coded Spiking Neural Networks for High-Dimensional Robotic Control, which achieves energy savings of up to 96.10% compared to traditional artificial neural networks, and DemoHLM, which enables generalizable loco-manipulation on a real humanoid robot from a single demonstration in simulation. Additionally, the development of modular and scalable frameworks, such as Open TeleDex, is facilitating the collection of high-quality demonstration data for imitation learning-based dexterous manipulation. Overall, these advances are paving the way for more efficient, adaptive, and robust robotic systems that can perform complex tasks in a variety of environments.
Advances in Robotic Manipulation and Control
Sources
Population-Coded Spiking Neural Networks for High-Dimensional Robotic Control
Towards a Unified Understanding of Robot Manipulation: A Comprehensive Survey
DemoHLM: From One Demonstration to Generalizable Humanoid Loco-Manipulation
Path and Motion Optimization for Efficient Multi-Location Inspection with Humanoid Robots
A Modular AIoT Framework for Low-Latency Real-Time Robotic Teleoperation in Smart Cities
Robot Soccer Kit: Omniwheel Tracked Soccer Robots for Education
Achieving Meaningful Collaboration: Worker-centered Design of a Physical Human-Robot Collaborative Blending Task
Robot Learning: A Tutorial
Fast Visuomotor Policy for Robotic Manipulation
Automated Behavior Planning for Fruit Tree Pruning via Redundant Robot Manipulators: Addressing the Behavior Planning Challenge
ALOHA2 Robot Kitchen Application Scenario Reproduction Report
Development of an Intuitive GUI for Non-Expert Teleoperation of Humanoid Robots
A Modular Object Detection System for Humanoid Robots Using YOLO
Choreographing Trash Cans: On Speculative Futures of Weak Robots in Public Spaces
Prescribed Performance Control of Deformable Object Manipulation in Spatial Latent Space
Restoring Noisy Demonstration for Imitation Learning With Diffusion Models
Open TeleDex: A Hardware-Agnostic Teleoperation System for Imitation Learning based Dexterous Manipulation
RL-100: Performant Robotic Manipulation with Real-World Reinforcement Learning
Design of Paper Robot Building Kits