53 research outputs found
Relational Future Captioning Model for Explaining Likely Collisions in Daily Tasks
Domestic service robots that support daily tasks are a promising solution for
elderly or disabled people. It is crucial for domestic service robots to
explain the collision risk before they perform actions. In this paper, our aim
is to generate a caption about a future event. We propose the Relational Future
Captioning Model (RFCM), a crossmodal language generation model for the future
captioning task. The RFCM has the Relational Self-Attention Encoder to extract
the relationships between events more effectively than the conventional
self-attention in transformers. We conducted comparison experiments, and the
results show the RFCM outperforms a baseline method on two datasets.Comment: Accepted for presentation at ICIP202
CrossMap Transformer: A Crossmodal Masked Path Transformer Using Double Back-Translation for Vision-and-Language Navigation
Navigation guided by natural language instructions is particularly suitable
for Domestic Service Robots that interacts naturally with users. This task
involves the prediction of a sequence of actions that leads to a specified
destination given a natural language navigation instruction. The task thus
requires the understanding of instructions, such as ``Walk out of the bathroom
and wait on the stairs that are on the right''. The Visual and Language
Navigation remains challenging, notably because it requires the exploration of
the environment and at the accurate following of a path specified by the
instructions to model the relationship between language and vision. To address
this, we propose the CrossMap Transformer network, which encodes the linguistic
and visual features to sequentially generate a path. The CrossMap transformer
is tied to a Transformer-based speaker that generates navigation instructions.
The two networks share common latent features, for mutual enhancement through a
double back translation model: Generated paths are translated into instructions
while generated instructions are translated into path The experimental results
show the benefits of our approach in terms of instruction understanding and
instruction generation.Comment: 8 pages, 5 figures, 5 tables. Submitted to IEEE Robotics and
Automation Letter
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