Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Imagine That! Leveraging Emergent Affordances for 3D Tool Synthesis   In this paper we explore the richness of information captured by the latent
space of a vision-based generative model. The model combines unsupervised
generative learning with a task-based performance predictor to learn and to
exploit task-relevant object affordances given visual observations from a
reaching task, involving a scenario and a stick-like tool. While the learned
embedding of the generative model captures factors of variation in 3D tool
geometry (e.g. length, width, and shape), the performance predictor identifies
sub-manifolds of the embedding that correlate with task success. Within a
variety of scenarios, we demonstrate that traversing the latent space via
backpropagation from the performance predictor allows us to imagine tools
appropriate for the task at hand. Our results indicate that affordances-like
the utility for reaching-are encoded along smooth trajectories in latent space.
Accessing these emergent affordances by considering only high-level performance
criteria (such as task success) enables an agent to manipulate tool geometries
in a targeted and deliberate way.

26
The categories of this paper are: bayes-an
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
24
The categories of this paper are: dg-ga
29
The categories of this paper are: funct-an