receptacle
Further Details
A.1 Dataset Details The 20 micro-variations of the 5 macro-variations of the scene were created with the rule of swapping at least two furniture pieces and perturbing the positions of a subset of the other furniture pieces. The occurrences of various furniture objects in these 100 micro-variations are illustrated in Figure 1. Several furniture objects such as'Beanbag' and'Chair' occur more frequently with multiple instances in a some scenes while others such as'Table 03' occur less frequently. We also analyze the object categories of all objects in the original 6 'FRL-apartment' space recreations. We map each of the 92 objects to a semantic category and list the counts per semantic category in a histogram in Figure 1. Since these spaces have a large kitchen area, there is a larger ratio of kitchen objects such as'Kitchen utensil' and'Bowl'. Top down views of the 5 'macro variations' of the scenes are shown in Figure 1. These variations are 5 semantically plausible configurations of furniture in the space generated by a 3D artist. Each surface is annotated with a bounding box, enabling procedural placement of objects on the surfaces. For each of these 5 variations, we generate 20 additional variations, giving 105 scene layouts. Objects are procedurally added on furniture and surfaces using the annotated supporting surface and containment volume information provided by ReplicaCAD.
Habitat 2.0: Training Home Assistants to Rearrange their Habitat
We introduce Habitat 2.0 (H2.0), a simulation platform for training virtual robots in interactive 3D environments and complex physics-enabled scenarios. We make comprehensive contributions to all levels of the embodied AI stack - data, simulation, and benchmark tasks.
A Appendix Contents
An example prompt for constructing PDDL models of the action "close a
A Supplementary Material This Supplementary Material section provides additional details and discussions to complement our
C Results in ALFRED's T ask and New T asks
WorldCoder,a Model-Based LLM Agent: Building World Models by Writing Code and Interacting with the Environment
We give a model-based agent that builds a Python program representing its knowledge of the world based on its interactions with the environment.
SupplementaryMaterialfor HandMeThat: Human-RobotCommunication inPhysicalandSocialEnvironments
In Section B, we summarize the statistics of the dataset. A.1 ObjectSpace Recall that HandMeThat uses an object-centric representation for states. Object hierarchy.HandMeThat classifies all categories into 5classes: location, receptacle, food, tool,andthing. Each class (except for"location") iscomposed ofmultiple subclasses, and each subclass contains several object categories. Intotal, there are155 object categories.