Predicting Dense and Context-aware Cost Maps for Semantic Robot Navigation

We investigate the task of object goal navigation in unknown environments where the target is specified by a semantic label (e.g. find a couch). Such a navigation task is especially challenging as it requires understanding of semantic context in diverse settings. Most of the prior work tackles this problem under the assumption of a discrete action policy whereas we present an approach with continuous control which brings it closer to real world applications. We propose a deep neural network architecture and loss function to predict dense cost maps that implicitly contain semantic context and guide the robot towards the semantic goal. We also present a novel way of fusing mid-level visual representations in our architecture to provide additional semantic cues for cost map prediction. The estimated cost maps are then used by a sampling-based model predictive controller (MPC) for generating continuous robot actions. The preliminary experiments suggest that the cost maps generated by our network are suitable for the MPC and can guide the agent to the semantic goal more efficiently than a baseline approach. The results also indicate the importance of mid-level representations for navigation by improving the success rate by 7 percentage points.

  • Published in:
    Perception and Navigation for Autonomous Robotics in Unstructured and Dynamic Environment Workshop
  • Type:
    Inproceedings
  • Authors:
    Goel, Yash; Vaskevicius, Narunas; Palmieri, Luigi; Chebrolu, Nived; Stachniss, Cyrill
  • Year:
    2023

Citation information

Goel, Yash; Vaskevicius, Narunas; Palmieri, Luigi; Chebrolu, Nived; Stachniss, Cyrill: Predicting Dense and Context-aware Cost Maps for Semantic Robot Navigation, Perception and Navigation for Autonomous Robotics in Unstructured and Dynamic Environment Workshop, 2023, https://arxiv.org/abs/2210.08952, Goel.etal.2023a,

Associated Lamarr Researchers

lamarr institute person Stachniss Cyrill e1663922306234 - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Prof. Dr. Cyrill Stachniss

Principal Investigator Embodied AI to the profile