4 citations · 18 across the 13 of their papers we have counts for
13 papers
Building Intelligent Agents with Neuro-Symbolic Concepts
Jiayuan Mao, Joshua B. Tenenbaum, Jiajun Wu
This article presents a concept-centric paradigm for building agents that can learn continually and reason flexibly. The concept-centric agent utilizes a vocabulary of neuro-symbol…
Multiagent Finetuning: Self Improvement with Diverse Reasoning Chains
Vighnesh Subramaniam, Yilun Du, Joshua B. Tenenbaum +3
Large language models (LLMs) have achieved remarkable performance in recent years but are fundamentally limited by the underlying training data. To improve models beyond the traini…
One-Shot Manipulation Strategy Learning by Making Contact Analogies
Yuyao Liu, Jiayuan Mao, Joshua Tenenbaum +2
We present a novel approach, MAGIC (manipulation analogies for generalizable intelligent contacts), for one-shot learning of manipulation strategies with fast and extensive general…
Few-Shot Task Learning through Inverse Generative Modeling
Aviv Netanyahu, Yilun Du, Antonia Bronars +4
Learning the intents of an agent, defined by its goals or motion style, is often extremely challenging from just a few examples. We refer to this problem as task concept learning a…
Potential Based Diffusion Motion Planning
Yunhao Luo, Chen Sun, Joshua B. Tenenbaum +1
Effective motion planning in high dimensional spaces is a long-standing open problem in robotics. One class of traditional motion planning algorithms corresponds to potential-based…
Compositional Image Decomposition with Diffusion Models
Jocelin Su, Nan Liu, Yanbo Wang +2
Given an image of a natural scene, we are able to quickly decompose it into a set of components such as objects, lighting, shadows, and foreground. We can then envision a scene whe…
Learning Iterative Reasoning through Energy Diffusion
Yilun Du, Jiayuan Mao, Joshua B. Tenenbaum
We introduce iterative reasoning through energy diffusion (IRED), a novel framework for learning to reason for a variety of tasks by formulating reasoning and decision-making probl…
HAZARD Challenge: Embodied Decision Making in Dynamically Changing Environments
Qinhong Zhou, Sunli Chen, Yisong Wang +6
Recent advances in high-fidelity virtual environments serve as one of the major driving forces for building intelligent embodied agents to perceive, reason and interact with the ph…
DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models
Tsun-Hsuan Wang, Juntian Zheng, Pingchuan Ma +6
Nature evolves creatures with a high complexity of morphological and behavioral intelligence, meanwhile computational methods lag in approaching that diversity and efficacy. Co-opt…
Video Language Planning
Yilun Du, Mengjiao Yang, Pete Florence +10
We are interested in enabling visual planning for complex long-horizon tasks in the space of generated videos and language, leveraging recent advances in large generative models pr…
Learning to Act from Actionless Videos through Dense Correspondences
Po-Chen Ko, Jiayuan Mao, Yilun Du +2
In this work, we present an approach to construct a video-based robot policy capable of reliably executing diverse tasks across different robots and environments from few video dem…
HandMeThat: Human-Robot Communication in Physical and Social Environments
Yanming Wan, Jiayuan Mao, Joshua B. Tenenbaum
We introduce HandMeThat, a benchmark for a holistic evaluation of instruction understanding and following in physical and social environments. While previous datasets primarily foc…
Compositional Foundation Models for Hierarchical Planning
Anurag Ajay, Seungwook Han, Yilun Du +7
To make effective decisions in novel environments with long-horizon goals, it is crucial to engage in hierarchical reasoning across spatial and temporal scales. This entails planni…