5 citations · 9 across the 9 of their papers we have counts for
10 papers
Neural Amortized Inference for Nested Multi-agent Reasoning
Kunal Jha, Tuan Anh Le, Chuanyang Jin +3
Multi-agent interactions, such as communication, teaching, and bluffing, often rely on higher-order social inference, i.e., understanding how others infer oneself. Such intricate r…
Summarize the Past to Predict the Future: Natural Language Descriptions of Context Boost Multimodal Object Interaction Anticipation
Razvan-George Pasca, Alexey Gavryushin, Muhammad Hamza +5
We study object interaction anticipation in egocentric videos. This task requires an understanding of the spatio-temporal context formed by past actions on objects, coined action c…
Reconstructing Action-Conditioned Human-Object Interactions Using Commonsense Knowledge Priors
Xi Wang, Gen Li, Yen-Ling Kuo +3
We present a method for inferring diverse 3D models of human-object interactions from images. Reasoning about how humans interact with objects in complex scenes from a single 2D im…
Incorporating Rich Social Interactions Into MDPs
Ravi Tejwani, Yen-Ling Kuo, Tianmin Shu +5
Much of what we do as humans is engage socially with other agents, a skill that robots must also eventually possess. We demonstrate that a rich theory of social interactions origin…
Trajectory Prediction with Linguistic Representations
Yen-Ling Kuo, Xin Huang, Andrei Barbu +4
Language allows humans to build mental models that interpret what is happening around them resulting in more accurate long-term predictions. We present a novel trajectory predictio…
Compositional Networks Enable Systematic Generalization for Grounded Language Understanding
Yen-Ling Kuo, Boris Katz, Andrei Barbu
Humans are remarkably flexible when understanding new sentences that include combinations of concepts they have never encountered before. Recent work has shown that while deep netw…
Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas
Yen-Ling Kuo, Boris Katz, Andrei Barbu
We demonstrate a reinforcement learning agent which uses a compositional recurrent neural network that takes as input an LTL formula and determines satisfying actions. The input LT…
Investigating the Decoders of Maximum Likelihood Sequence Models: A Look-ahead Approach
Yu-Siang Wang, Yen-Ling Kuo, Boris Katz
We demonstrate how we can practically incorporate multi-step future information into a decoder of maximum likelihood sequence models. We propose a "k-step look-ahead" module to con…
Deep compositional robotic planners that follow natural language commands
Yen-Ling Kuo, Boris Katz, Andrei Barbu
We demonstrate how a sampling-based robotic planner can be augmented to learn to understand a sequence of natural language commands in a continuous configuration space to move and…
Deep sequential models for sampling-based planning
Yen-Ling Kuo, Andrei Barbu, Boris Katz
We demonstrate how a sequence model and a sampling-based planner can influence each other to produce efficient plans and how such a model can automatically learn to take advantage…