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Found 15 papers, 6 papers with code
UAD: Unsupervised Affordance Distillation for Generalization in Robotic Manipulation
Understanding fine-grained object affordances is imperative for robots to manipulate objects in unstructured environments given open-ended task instructions.
BEHAVIOR Vision Suite: Customizable Dataset Generation via Simulation
We introduce the BEHAVIOR Vision Suite (BVS), a set of tools and assets to generate fully customized synthetic data for systematic evaluation of computer vision models, based on the newly developed embodied AI benchmark, BEHAVIOR-1K.
BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with 1,000 Everyday Activities and Realistic Simulation
We present BEHAVIOR-1K, a comprehensive simulation benchmark for human-centered robotics.
Chain of Code: Reasoning with a Language Model-Augmented Code Emulator
For example, consider prompting an LM to write code that counts the number of times it detects sarcasm in an essay: the LM may struggle to write an implementation for "detect_sarcasm(string)" that can be executed by the interpreter (handling the edge cases would be insurmountable).
Principles and Guidelines for Evaluating Social Robot Navigation Algorithms
A major challenge to deploying robots widely is navigation in human-populated environments, commonly referred to as social robot navigation.
Task-Driven Graph Attention for Hierarchical Relational Object Navigation
Embodied AI agents in large scenes often need to navigate to find objects.
Sonicverse: A Multisensory Simulation Platform for Embodied Household Agents that See and Hear
We introduce Sonicverse, a multisensory simulation platform with integrated audio-visual simulation for training household agents that can both see and hear.
Modeling Dynamic Environments with Scene Graph Memory
We evaluate our method in the Dynamic House Simulator, a new benchmark that creates diverse dynamic graphs following the semantic patterns typically seen at homes, and show that NEP can be trained to predict the locations of objects in a variety of environments with diverse object movement dynamics, outperforming baselines both in terms of new scene adaptability and overall accuracy.
Retrospectives on the Embodied AI Workshop
We present a retrospective on the state of Embodied AI research.
BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environments
We introduce BEHAVIOR, a benchmark for embodied AI with 100 activities in simulation, spanning a range of everyday household chores such as cleaning, maintenance, and food preparation.
iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks
We evaluate the new capabilities of iGibson 2. 0 to enable robot learning of novel tasks, in the hope of demonstrating the potential of this new simulator to support new research in embodied AI.
IGibson 1.0: a Simulation Environment for Interactive Tasks in Large Realistic Scenes
We present iGibson 1. 0, a novel simulation environment to develop robotic solutions for interactive tasks in large-scale realistic scenes.
ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation
To validate our method, we apply ReLMoGen to two types of tasks: 1) Interactive Navigation tasks, navigation problems where interactions with the environment are required to reach the destination, and 2) Mobile Manipulation tasks, manipulation tasks that require moving the robot base.
Interactive Gibson Benchmark (iGibson 0.5): A Benchmark for Interactive Navigation in Cluttered Environments
We present Interactive Gibson Benchmark, the first comprehensive benchmark for training and evaluating Interactive Navigation: robot navigation strategies where physical interaction with objects is allowed and even encouraged to accomplish a task.
HRL4IN: Hierarchical Reinforcement Learning for Interactive Navigation with Mobile Manipulators
Different from other HRL solutions, HRL4IN handles the heterogeneous nature of the Interactive Navigation task by creating subgoals in different spaces in different phases of the task.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
