1 citations · 1 across the 5 of their papers we have counts for
5 papers
Organizational Principles Enable Collective Intelligence in Embodied AI
Zhengran Ji, Jonathan Hyun, Boyuan Chen
Collective intelligence depends not only on the capabilities of individual members, but also on how those members are organized. Yet artificial multi-agent systems are typically as…
Pref-GUIDE: Continual Policy Learning from Real-Time Human Feedback via Preference-Based Learning
Zhengran Ji, Boyuan Chen
Training reinforcement learning agents with human feedback is crucial when task objectives are difficult to specify through dense reward functions. While prior methods rely on offl…
GUIDE: Real-Time Human-Shaped Agents
Lingyu Zhang, Zhengran Ji, Nicholas R Waytowich +1
The recent rapid advancement of machine learning has been driven by increasingly powerful models with the growing availability of training data and computational resources. However…
Enabling Multi-Robot Collaboration from Single-Human Guidance
Zhengran Ji, Lingyu Zhang, Paul Sajda +1
Learning collaborative behaviors is essential for multi-agent systems. Traditionally, multi-agent reinforcement learning solves this implicitly through a joint reward and centraliz…
CREW: Facilitating Human-AI Teaming Research
Lingyu Zhang, Zhengran Ji, Boyuan Chen
With the increasing deployment of artificial intelligence (AI) technologies, the potential of humans working with AI agents has been growing at a great speed. Human-AI teaming is a…