1 code implementation • 13 Feb 2024 • Wilbert Pumacay, Ishika Singh, Jiafei Duan, Ranjay Krishna, Jesse Thomason, Dieter Fox
To realize effective large-scale, real-world robotic applications, we must evaluate how well our robot policies adapt to changes in environmental conditions.
Ranked #1 on Robot Manipulation Generalization on The COLOSSEUM
no code implementations • 7 Nov 2023 • Ainaz Eftekhar, Kuo-Hao Zeng, Jiafei Duan, Ali Farhadi, Ani Kembhavi, Ranjay Krishna
Inspired by selective attention in humans-the process through which people filter their perception based on their experiences, knowledge, and the task at hand-we introduce a parameter-efficient approach to filter visual stimuli for embodied AI.
no code implementations • 10 Oct 2023 • Yi Ru Wang, Jiafei Duan, Dieter Fox, Siddhartha Srinivasa
To address this gap, we introduce NEWTON, a repository and benchmark for evaluating the physics reasoning skills of LLMs.
no code implementations • 23 Jun 2023 • Jiafei Duan, Yi Ru Wang, Mohit Shridhar, Dieter Fox, Ranjay Krishna
By contrast, we introduce AR2-D2: a system for collecting demonstrations which (1) does not require people with specialized training, (2) does not require any real robots during data collection, and therefore, (3) enables manipulation of diverse objects with a real robot.
no code implementations • 6 Mar 2023 • Jenny Zhang, Samson Yu, Jiafei Duan, Cheston Tan
This paper presents a framework for training an agent to actively request help in object-goal navigation tasks, with feedback indicating the location of the target object in its field of view.
no code implementations • 21 Jun 2022 • Jiafei Duan, Samson Yu, Nicholas Tan, Li Yi, Cheston Tan
Humans with an average level of social cognition can infer the beliefs of others based solely on the nonverbal communication signals (e. g. gaze, gesture, pose and contextual information) exhibited during social interactions.
1 code implementation • 20 Jun 2022 • Jenny Zhang, Samson Yu, Jiafei Duan, Cheston Tan
In reality, it is often more efficient to ask for help than to search the entire space to find an object with an unknown location.
no code implementations • 10 Jun 2022 • Jieyi Ye, Jiafei Duan, Samson Yu, Bihan Wen, Cheston Tan
How can the most common 1, 000 concepts (89\% of everyday use) be learnt in a naturalistic children's setting?
no code implementations • 14 Feb 2022 • Jiafei Duan, Arijit Dasgupta, Jason Fischer, Cheston Tan
Despite the wide range of work in physical reasoning for machine cognition, there is a scarcity of reviews that organize and group these deep learning approaches.
no code implementations • 16 Nov 2021 • Arijit Dasgupta, Jiafei Duan, Marcelo H. Ang Jr, Yi Lin, Su-hua Wang, Renée Baillargeon, Cheston Tan
Recent work in computer vision and cognitive reasoning has given rise to an increasing adoption of the Violation-of-Expectation (VoE) paradigm in synthetic datasets.
1 code implementation • 12 Oct 2021 • Arijit Dasgupta, Jiafei Duan, Marcelo H. Ang Jr, Cheston Tan
Recent work in cognitive reasoning and computer vision has engendered an increasing popularity for the Violation-of-Expectation (VoE) paradigm in synthetic datasets.
no code implementations • 10 Sep 2021 • Jiafei Duan, Samson Yu, Soujanya Poria, Bihan Wen, Cheston Tan
However, there is a lack of intuitive physics models that are tested on long physical interaction sequences with multiple interactions among different objects.
Ranked #1 on Semantic Object Interaction Classification on SPACE
2 code implementations • 13 Aug 2021 • Jiafei Duan, Samson Yu Bai Jian, Cheston Tan
We then further evaluate it with a state-of-the-art physics-based deep model and show that the SPACE dataset improves the learning of intuitive physics with an approach inspired by curriculum learning.
no code implementations • 8 Mar 2021 • Jiafei Duan, Samson Yu, Hui Li Tan, Hongyuan Zhu, Cheston Tan
This paper aims to provide an encyclopedic survey for the field of embodied AI, from its simulators to its research.
1 code implementation • 3 Oct 2020 • Jiafei Duan, Samson Yu, Hui Li Tan, Cheston Tan
The problem of task planning for artificial agents remains largely unsolved.