58 citations · 118 across the 10 of their papers we have counts for
14 papers
Discovering Generalizable Spatial Goal Representations via Graph-based Active Reward Learning
Aviv Netanyahu, Tianmin Shu, Joshua Tenenbaum +1
In this work, we consider one-shot imitation learning for object rearrangement tasks, where an AI agent needs to watch a single expert demonstration and learn to perform the same t…
Coordinating Policies Among Multiple Agents via an Intelligent Communication Channel
Dianbo Liu, Vedant Shah, Oussama Boussif +6
In Multi-Agent Reinforcement Learning (MARL), specialized channels are often introduced that allow agents to communicate directly with one another. In this paper, we propose an alt…
PHASE: PHysically-grounded Abstract Social Events for Machine Social Perception
Aviv Netanyahu, Tianmin Shu, Boris Katz +2
The ability to perceive and reason about social interactions in the context of physical environments is core to human social intelligence and human-machine cooperation. However, no…
AGENT: A Benchmark for Core Psychological Reasoning
Tianmin Shu, Abhishek Bhandwaldar, Chuang Gan +6
For machine agents to successfully interact with humans in real-world settings, they will need to develop an understanding of human mental life. Intuitive psychology, the ability t…
Active Visual Information Gathering for Vision-Language Navigation
Hanqing Wang, Wenguan Wang, Tianmin Shu +2
Vision-language navigation (VLN) is the task of entailing an agent to carry out navigational instructions inside photo-realistic environments. One of the key challenges in VLN is h…
Joint Mind Modeling for Explanation Generation in Complex Human-Robot Collaborative Tasks
Xiaofeng Gao, Ran Gong, Yizhou Zhao +3
Human collaborators can effectively communicate with their partners to finish a common task by inferring each other's mental states (e.g., goals, beliefs, and desires). Such mind-a…