RobotDancing: Residual-Action Reinforcement Learning Enables Robust Long-Horizon Humanoid Motion Tracking
Long-horizon, high-dynamic motion tracking on humanoids remains brittle: retargeted reference motions are typically kinematically plausible but dynamically inconsistent with the robot, so small tracking errors accumulate and eventually destabilize control.We present RobotDancing, a practical single-stage reinforcement learning recipe that tracks retargeted motions by predicting reference-conditioned residual joint targets. By parameterizing actions as residual corrections on…
Resilient Multi-Agent Deep Reinforcement Learning for Automatic Generation Control Under Adversarial Perturbations
An efficient method is proposed to improve the cyber-physical resilience of multi-agent deep deterministic policy gradient (MADDPG)-based automatic generation control (AGC) against malicious adversarial perturbations in the agents’ observation space. We first provide a sensitivity analysis demonstrating that the Jacobian of deep neural network (DNN) policies can amplify small perturbations into large and suboptimal control…
Multi-Agent Reinforcement Learning via Agent-Specific Preference
Multi-agent reinforcement learning (MARL) is a powerful framework for solving complex collaborative tasks, but it relies heavily on well-defined global reward functions. Designing such rewards is challenging, especially in systems with heterogeneous agents, where a single scalar objective may fail to capture diverse behaviors. In this paper, we introduce Multi-AGent Preference-Integrated lEarning (MAGPIE), which addresses…
Out-of-Distribution Modular Hospital Fit-Out Scheduling via Memory-Augmented Deep Reinforcement Learning
Efficient resource scheduling for modular hospital fit-out accelerates the deployment of medical facilities, ultimately determining the project inauguration timeline. However, fit-out scheduling faces the real-time coordination of multiple rooms and workers, complex matching relationships between multi-skilled workers and diverse operations, and the dynamic accumulation of worker fatigue. These factors lead to varying evolutions of worker…
Video Friday: An Italian Humanoid Comes to Life
Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion.Summer School on Multi-Robot Systems: 29 July–4 August 2026, PRAGUEActuate 2026: 18–19 August 2026, SAN FRANCISCOIROS…
Video Friday: Your Robot Surgeon Will See You Now
Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion.Summer School on Multi-Robot Systems: 29 July–4 August 2026, PRAGUEActuate 2026: 18–19 August 2026, SAN FRANCISCOIROS…
Video Friday: A World Cup for Robots
Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion.RSS 2026: 13–17 July 2026, SYDNEYSummer School on Multi-Robot Systems: 29 July–4 August 2026, PRAGUEActuate 2026:…
Generative Adversarial Self-Imitation Learning With Large Language Model Feedback for Robot Control and Navigation
Deep reinforcement learning (DRL) has achieved great success in many simulated and real-world robotic tasks. However, the difficulty of designing efficient and dense reward functions makes applying DRL to tackle complex long-horizon and open-world tasks a great challenge. Generative adversarial imitation learning (GAIL) can directly learn policies from the expert trajectories and generalize well in…
MA-APD: Multiagent Asynchronous Probability-Decomposed Policy Gradient for Time-Constrained Moving Target Search
This article investigates the time-constrained multirobot efficient search (MuRES) problem, focusing on the asynchronous coordination among multiple robots. To the best of authors’ knowledge, almost all MuRES solutions adopt the synchronous decision-making framework, wherein the robots act simultaneously. The synchronous assumption simplifies the MuRES problem formulation and facilitates the development of coordination search strategies. However,…
STTRL-DVO: Transformer-Based Reinforcement Learning for Robust Dynamic Target Tracking in Cluttered Environments
Magnetic microrobots capable of autonomous operation hold significant promise for critical cell and small creature manipulation tasks, including trapping, transportation, sorting, etc. Although conventional microrobot navigation methods have shown notable performance, they often lack adaptability to novel environments. To address these limitations, we propose a learning-based framework for real-world microrobot navigation and dynamic target tracking.…