ObjectReact: Learning Object-Relative Control for Visual Navigation

Sourav Garg*1, Dustin Craggs*1, Vineeth Bhat2, Lachlan Mares1,
Stefan Podgorski1, Madhava Krishna2, Feras Dayoub1, Ian Reid1,3
1University of Adelaide, Australia, 2IIIT Hyderabad, India, 3MBZUAI, UAE,
* denotes equal contribution
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To be presented at CoRL 2025, Seoul, Korea.
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ObjectReact agent navigates using a learnt controller conditioned on a WayObject Costmap representation, which breaks down visible objects by distance to the goal. Object distances are estimated using an object-level relative 3D scene graph.

Abstract

Visual navigation using only a single camera and a topological map has recently become an appealing alternative to methods that require additional sensors and 3D maps. This is typically achieved through an image-relative approach to estimating control from a given pair of current observation and subgoal image. However, image-level representations of the world have limitations because images are strictly tied to the agent's pose and embodiment. In contrast, objects, being a property of the map, offer an embodiment- and trajectory-invariant world representation. In this work, we present a new paradigm of learning object-relative control that exhibits several desirable characteristics:

a) New routes can be traversed without strictly requiring to imitate prior experience,
b) The control prediction problem can be decoupled from solving the image matching problem, and
c) High invariance can be achieved in cross-embodiment deployment for variations across both training-testing and mapping-execution settings.

We propose a topometric map representation in the form of a relative 3D scene graph, which is used to obtain more informative object-level global path planning costs. We train a local controller, dubbed ObjectReact, conditioned directly on a high-level "WayObject Costmap" representation that eliminates the need for an explicit RGB input. We demonstrate the advantages of learning object-relative control over its image-relative counterpart across sensor height variations and multiple navigation tasks that challenge the underlying spatial understanding capability, e.g., navigating a map trajectory in the reverse direction. We further show that our sim-only policy is able to generalize well to real-world indoor environments.

ObjectReact Pipeline

a) Mapping: We construct a topometric map as a relative 3D scene graph, where image segments are used as object nodes, which are connected intra-image using 3D Euclidean distances and inter-image using object association. b) Execution: Given the map, we localize each of the query objects and compute its path to the goal node; we assign these path lengths to the object's segmentation mask, forming a “WayObject Costmap” for control prediction. c) Training: We train a model to learn an “ObjectReact” controller that predicts trajectory rollouts from WayObject Costmaps.

Cross-Embodiment Deployment

Phone Video: Used to generate the object-level map.

a) Cross-embodiment deployment between mapping and execution

b) Avoids new obstacles not present during mapping

c) Low-light adaptation

d) Alt goal tasks

One map video (taken using a phone camera), can be used for deployment a) on a quadruped robot with a different camera, b) with new obstacles, c) under reduced lighting conditions, and d) navigating to an alternative goal that was on the periphery of the mapping run (deployment shown at 8x speed).

Shortcut Task

Long mapping video: The mapping run takes a longer
path to the goal (using a phone camera).

Deployment: The robot takes a direct path
to the cardboard cutout goal.

ObjectReact can take shortcuts that were not demonstrated during the mapping run (deployment shown at 8x speed).

New Tasks

Each column shows a topdown view with the prior experience (map) trajectory displayed as a purple path from the purple circle (start) to green point (goal). Imitate is akin to teach-and-repeat. In Alt-Goal, the goal object is previously seen but is unvisited. In Shortcut, the prior mapping trajectory is made longer, and agents are able to take a shortcut to achieve the goal using a shorter path. In Reverse, the agent must travel in the opposite direction to the prior mapping run.

Results

ObjectReact (Object Relative) performs similarly for the simpler Imitate task, but significantly outperforms its image relative counterpart in all other cases.

Rollout examples. ObjectReact prefers to navigate towards areas of lower estimated cost/closer proximity to the goal (green-yellow), and can avoid obstacles not present during the mapping run.

Other Deployment Videos

BibTeX


      @inproceedings{garg2025objectreact,
        title={ObjectReact: Learning Object-Relative Control for Visual Navigation},
        author={Garg, Sourav and Craggs, Dustin and Bhat, Vineeth and Mares, Lachlan and Podgorski, Stefan and Krishna, Madhava and Dayoub, Feras and Reid, Ian},
        booktitle={Conference on Robot Learning},
        year={2025},
        organization={PMLR}
      }