1 citations · 1 across the 7 of their papers we have counts for
7 papers
Interpreting Control Latents for System Identification via Conditional Flow Matching
Dingqi Zhang, Ruiqi Zhang, Mark W. Mueller
Latent-conditioned adaptive policies can control robots across changing dynamics, but their learned latents remain internal representations of the policy rather than physical model…
CDCP: Conditional Diffusion Model with Contextual Prompts for Multi-task Offline Safe Reinforcement Learning
Jiayi Guan, Tianle Zhang, Li Shen +8
Multi-task offline safe reinforcement learning (RL) promises to learn a shared optimal safe policy from offline data across multiple tasks. This paradigm provides an effective mean…
Autonomous Close-Proximity Photovoltaic Panel Coating Using a Quadcopter
Dimitri Jacquemont, Carlo Bosio, Teaya Yang +7
Photovoltaic (PV) panels are becoming increasingly widespread in the domain of renewable energy, and thus, small efficiency gains can have massive effects. Anti-reflective and self…
Duawlfin: A Drone with Unified Actuation for Wheeled Locomotion and Flight Operation
Jerry Tang, Ruiqi Zhang, Kaan Beyduz +5
This paper presents Duawlfin, a drone with unified actuation for wheeled locomotion and flight operation that achieves efficient, bidirectional ground mobility. Unlike existing hyb…
ProxFly: Robust Control for Close Proximity Quadcopter Flight via Residual Reinforcement Learning
Ruiqi Zhang, Dingqi Zhang, Mark W. Mueller
This paper proposes the ProxFly, a residual deep Reinforcement Learning (RL)-based controller for close proximity quadcopter flight. Specifically, we design a residual module on to…
Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey
Ruiqi Zhang, Jing Hou, Florian Walter +7
Reinforcement Learning (RL) is a potent tool for sequential decision-making and has achieved performance surpassing human capabilities across many challenging real-world tasks. As…
Spreeze: High-Throughput Parallel Reinforcement Learning Framework
Jing Hou, Guang Chen, Ruiqi Zhang +3
The promotion of large-scale applications of reinforcement learning (RL) requires efficient training computation. While existing parallel RL frameworks encompass a variety of RL al…