Siyuan Qi

About

Siyuan Qi is a leading researcher at the intersection of robotics, artificial intelligence, and human-robot interaction. His work focuses on making autonomous systems not only more capable, but also more transparent and trustworthy. Qi’s most influential contribution, “A Tale of Two Explanations” (132 citations), investigates how robots can explain their decisions to build human trust—a critical step for deploying AI in high-stakes settings. He also pioneers methods for complex manipulation, as seen in “Feeling the Force” (66 citations), where imitation learning integrates force and pose to enable robots to perform multi-stage tasks like opening medicine bottles. In multi-agent systems, Qi’s “Intent-Aware Multi-Agent Reinforcement Learning” (35 citations) introduces a planning framework where agents reason about each other’s goals, advancing coordination in shared environments. Additionally, he developed VRGym (25 citations), a virtual reality testbed for realistic human-robot interaction that bridges robotics, machine learning, and cognitive science. With recent work on differentiable model-based reinforcement learning, Qi continues to push the boundaries of efficient, explainable, and collaborative AI systems.

Research Focus

Key Achievements

Top Papers

1. 1
   A tale of two explanations: Enhancing human trust by explaining robot behavior

   132 citations · 2019
2. 2
   Feeling the force: Integrating force and pose for fluent discovery through imitation learning to open medicine bottles

   66 citations · 2017
3. 3
   Intent-Aware Multi-Agent Reinforcement Learning

   35 citations · 2018
4. 4
   VRGym

   25 citations · 2019
5. 5
   Intent-aware Multi-agent Reinforcement Learning

   3 citations · 2018
6. 6
   Differentiable Information Enhanced Model-Based Reinforcement Learning

   2 citations · 2025

Key Collaborators

Contact & Links