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Found 15 papers, 5 papers with code
THE COLOSSEUM: A Benchmark for Evaluating Generalization for Robotic Manipulation
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
Selective Visual Representations Improve Convergence and Generalization for Embodied AI
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.
NEWTON: Are Large Language Models Capable of Physical Reasoning?
To address this gap, we introduce NEWTON, a repository and benchmark for evaluating the physics reasoning skills of LLMs.
AR2-D2:Training a Robot Without a Robot
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.
Robustness of Utilizing Feedback in Embodied Visual Navigation
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.
BOSS: A Benchmark for Human Belief Prediction in Object-context Scenarios
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.
Good Time to Ask: A Learning Framework for Asking for Help in Embodied Visual Navigation
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.
ABCDE: An Agent-Based Cognitive Development Environment
How can the most common 1, 000 concepts (89\% of everyday use) be learnt in a naturalistic children's setting?
A Survey on Machine Learning Approaches for Modelling Intuitive Physics
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.
A Benchmark for Modeling Violation-of-Expectation in Physical Reasoning Across Event Categories
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.
AVoE: A Synthetic 3D Dataset on Understanding Violation of Expectation for Artificial Cognition
Recent work in cognitive reasoning and computer vision has engendered an increasing popularity for the Violation-of-Expectation (VoE) paradigm in synthetic datasets.
PIP: Physical Interaction Prediction via Mental Simulation with Span Selection
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
SPACE: A Simulator for Physical Interactions and Causal Learning in 3D Environments
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.
A Survey of Embodied AI: From Simulators to Research Tasks
This paper aims to provide an encyclopedic survey for the field of embodied AI, from its simulators to its research.
Actionet: An Interactive End-To-End Platform For Task-Based Data Collection And Augmentation In 3D Environment
The problem of task planning for artificial agents remains largely unsolved.
