StructDiffusion: Language-guided creation of physically-valid structures using unseen objects
Robots operating in human environments must be able to rearrange objects into
semantically-meaningful configurations, even if these objects are previously unseen. In this …
semantically-meaningful configurations, even if these objects are previously unseen. In this …
Structdiffusion: Object-centric diffusion for semantic rearrangement of novel objects
Robots operating in human environments must be able to rearrange objects into
semantically-meaningful configurations, even if these objects are previously unseen. In this …
semantically-meaningful configurations, even if these objects are previously unseen. In this …
Leveraging commonsense knowledge from large language models for task and motion planning
Multi-object rearrangement is a crucial skill for service robots, and commonsense reasoning
is frequently needed in this process. However, achieving commonsense arrangements …
is frequently needed in this process. However, achieving commonsense arrangements …
Structformer: Learning spatial structure for language-guided semantic rearrangement of novel objects
Geometric organization of objects into semantically meaningful arrangements pervades the
built world. As such, assistive robots operating in warehouses, offices, and homes would …
built world. As such, assistive robots operating in warehouses, offices, and homes would …
Latent space planning for multi-object manipulation with environment-aware relational classifiers
Objects rarely sit in isolation in everyday human environments. If we want robots to operate
and perform tasks in our human environments, they must understand how the objects they …
and perform tasks in our human environments, they must understand how the objects they …
Dream2Real: Zero-shot 3D object rearrangement with vision-language models
We introduce Dream2Real, a robotics framework which integrates vision-language models
(VLMs) trained on 2D data into a 3D object rearrangement pipeline. This is achieved by the …
(VLMs) trained on 2D data into a 3D object rearrangement pipeline. This is achieved by the …
Predicting stable configurations for semantic placement of novel objects
Human environments contain numerous objects configured in a variety of arrangements.
Our goal is to enable robots to repose previously unseen objects according to learned …
Our goal is to enable robots to repose previously unseen objects according to learned …
Physically grounded vision-language models for robotic manipulation
Recent advances in vision-language models (VLMs) have led to improved performance on
tasks such as visual question answering and image captioning. Consequently, these models …
tasks such as visual question answering and image captioning. Consequently, these models …
Conceptgraphs: Open-vocabulary 3d scene graphs for perception and planning
For robots to perform a wide variety of tasks, they require a 3D representation of the world
that is semantically rich, yet compact and efficient for task-driven perception and planning …
that is semantically rich, yet compact and efficient for task-driven perception and planning …
Task and motion planning with large language models for object rearrangement
Multi-object rearrangement is a crucial skill for service robots, and commonsense reasoning
is frequently needed in this process. However, achieving commonsense arrangements …
is frequently needed in this process. However, achieving commonsense arrangements …