Guiding Robots Home: Learning to Navigate the Final Stretch with Vision Alone

Author: Denis Avetisyan

Researchers have developed a new imitation learning approach that enables robots to perform precise last-meter navigation using only RGB camera input.

The system achieves robust, manipulation-ready navigation through an object-centric imitation learning framework, bridging global path planning with precise last-meter adjustments refined by multi-view RGB observations and maintaining accuracy even amidst distractions, ultimately enabling the robot to reach a goal observation with a precise pose.

This work introduces an object-centric framework for visual grounding and manipulation-ready positioning via single-instance RGB demonstrations.

Achieving manipulation-ready positioning remains a critical challenge for mobile robots despite advances in navigation. This is addressed in ‘Learning Category-level Last-meter Navigation from RGB Demonstrations of a Single-instance’, which introduces an imitation learning framework for precise last-meter navigation using only onboard RGB cameras. By focusing on object-centric representations and visual grounding, the system generalizes to unseen objects within a category, achieving high success rates in both edge and object alignment. Could this RGB-only approach unlock truly scalable and adaptable mobile manipulation capabilities without reliance on depth sensors or prior maps?

Beyond Spatial Awareness: Embracing Object-Centric Navigation

Conventional robotic navigation systems frequently falter when confronted with real-world complexity, not because of mechanical limitations, but due to a fundamental deficit in comprehension. These systems primarily operate on geometric data – distances, angles, and spatial maps – processing environments as collections of obstacles and free space. This approach proves inadequate when tasks require understanding what those spaces contain; a robot interpreting a hallway simply as a clear path fails to differentiate between an open doorway and a blocked one, or to recognize objects relevant to its goals. Consequently, even sophisticated path planning algorithms can lead to failures in task completion, highlighting the critical need for robots to move beyond purely spatial reasoning and embrace semantic understanding of their surroundings.

Robotic systems traditionally rely on spatial coordinates for navigation, a method proving increasingly brittle in dynamic and unpredictable environments. A fundamental shift towards object-centricity addresses this limitation by prioritizing reasoning about identifiable objects – chairs, doorways, people – instead of simply calculating distances and angles. This approach fosters robustness because object permanence and semantic understanding allow a robot to continue functioning even with partial sensor data or unexpected obstacles; a robot ‘knowing’ it is approaching a table, rather than merely registering a collection of points, enables it to adapt its path if the table is partially obscured. Moreover, object-based navigation facilitates higher-level task completion, allowing robots to perform complex actions like “bring me the book on the table” which are impossible with purely coordinate-based systems. Ultimately, embracing object-centricity is not merely about improving navigation, but about enabling robots to interact with the world in a more intelligent and flexible manner.

Conventional robotic navigation prioritizes spatial data – coordinates and distances – to chart a course, often leading to brittle performance when encountering unexpected obstacles or dynamic environments. However, a more resilient approach centers on object-centric navigation, where the robot focuses on identifying and reasoning about the objects within its surroundings. Instead of simply calculating a path to a set of coordinates, the system determines “what” is being approached – a table, a doorway, a person – and adjusts its actions accordingly. This shift allows for more adaptable behavior; a robot aiming for “the table” can reroute around a chair without failing, while a system focused solely on coordinates would likely collide. By understanding the semantic meaning of its environment, rather than just its geometry, the robot achieves a level of robustness closer to human intuition and enabling it to complete complex tasks in unstructured spaces.

The training environment utilizes a green chair as the target object and AprilTags for both automating expert demonstrations and quantitatively evaluating the robot's performance in reaching a designated goal pose. — The training environment utilizes a green chair as the target object and AprilTags for both automating expert demonstrations and quantitatively evaluating the robot’s performance in reaching a designated goal pose.

Perceiving the World: Robust Object Identification

Accurate object identification in visual perception systems relies on the ability to effectively partition an image into multiple regions, a process known as segmentation. Advanced segmentation techniques move beyond simple pixel classification by identifying and delineating object boundaries, even in the presence of occlusion, varying lighting conditions, and complex backgrounds. These techniques often employ deep learning architectures, such as convolutional neural networks (CNNs), to learn hierarchical representations of visual features, enabling the system to distinguish between different objects and their components. The resulting segmented image provides a more structured and interpretable representation of the scene, facilitating downstream tasks like object recognition, tracking, and scene understanding.

The DINOv2 vision encoder is a self-supervised visual transformer model pretrained on a large dataset of images, allowing it to generate robust feature representations. These features are extracted through a multi-layer architecture that learns contextual relationships within images, enabling the identification of objects regardless of variations in pose, lighting, or occlusion. Specifically, DINOv2 employs a knowledge distillation approach with a student-teacher network to learn discriminative features without relying on manual annotations. The resulting feature maps provide a strong basis for downstream tasks such as semantic segmentation, where each pixel is classified, and instance segmentation, which differentiates individual objects within a scene, ultimately contributing to reliable object isolation.

The Segmentation Module utilizes the DINOv2 vision encoder to generate pixel-level masks identifying target objects within a scene. This process involves extracting feature maps from the input image via DINOv2, which are then processed to delineate object boundaries and create precise segmentation masks. These masks effectively isolate the desired objects from the background and other elements in the scene, providing a critical input for downstream tasks such as path planning and navigation. The resulting segmented objects are represented as distinct regions, enabling the system to focus computational resources and navigational strategies on relevant areas of the environment.

This model predicts actions by encoding observations and goals into feature embeddings, using a segmentation module to identify objects, and then decoding these representations-along with bounding box coordinates-through a feedforward network to determine the optimal action.

Precision in Motion: From Perception to Actionable Control

Last-Meter Navigation addresses the challenge of achieving precise control during the final stages of an approach to a target object. This phase necessitates significantly higher accuracy than broader navigational tasks, focusing on the robot’s ability to maneuver within a limited space immediately surrounding the target. The goal is to facilitate interaction with the target, such as docking, grasping, or alignment, and requires managing both translational and rotational errors. Successful Last-Meter Navigation is characterized by minimizing discrepancies between the robot’s pose and the desired pose relative to the target object, ultimately enabling reliable execution of subsequent manipulation tasks.

The Aim My Robot and MoTo policies operate by generating a discrete set of feasible docking points during the final approach phase. These policies prioritize both positional accuracy and computational efficiency by employing sampling-based planning techniques to identify viable locations for the robot to achieve a stable docking configuration. The resulting docking points are evaluated based on criteria such as collision avoidance, reachability, and alignment with the target object’s features, allowing the robot to select the optimal location for precise final maneuvering. This approach enables rapid computation of docking solutions without requiring exhaustive search of the entire configuration space.

Object and edge alignment tasks build upon initial navigation to achieve manipulation-ready precision. The implemented framework consistently positions a robot within 0.3 meters of translational error and 9 degrees of orientational error relative to a target object or edge. This level of accuracy is critical for subsequent manipulation operations, ensuring successful grasping and interaction with the environment. Performance is maintained across a variety of target geometries and lighting conditions, demonstrating the robustness of the alignment procedures.

DinoScoreAux consistently achieves the highest success rates in last-meter navigation, both with edge and object alignment, demonstrating robust generalization to unseen chair instances.

Learning to Navigate: Imitation and Beyond

Imitation Learning facilitates rapid robotic navigation skill acquisition by leveraging demonstrations of successful behavior. This approach bypasses the need for extensive reward function engineering, instead directly learning a policy from expert data – typically consisting of state-action pairs recorded during human or otherwise successful autonomous navigation. The robot learns to map observed states to corresponding actions, effectively mimicking the demonstrated strategy. This is particularly advantageous in complex environments where defining an optimal reward function is challenging or time-consuming, allowing robots to quickly achieve functional navigation capabilities through observational learning.

Accurate robot localization is fundamental for both training and validating navigation policies; this is frequently accomplished through Ground Truth Localization systems. These systems, such as those utilizing AprilTag fiducial markers, provide precise, known poses – that is, position and orientation – enabling the creation of datasets where the robot’s true state is known. This data is then used to supervise learning algorithms and to objectively measure the performance of trained navigation policies. Without accurate ground truth, evaluating progress and comparing different approaches becomes significantly more difficult, as observed behavior cannot be definitively attributed to the policy itself versus localization error.

The NoMaD and PoliFormer policies leverage imitation learning to achieve autonomous navigation utilizing only RGB visual input. Performance evaluations across ten previously unseen scenarios demonstrate an average navigation success rate of 75%. Specifically, the system achieves 85% success in translation and 95% in orientation tasks within outdoor environments. Indoor performance metrics indicate 79% translation success and 91% orientation success, demonstrating a capacity for robust navigation across diverse settings without reliance on depth sensors or LiDAR.

The robot’s final pose (red arrow) is evaluated against the ground-truth pose (blue arrow) in both edge alignment and object alignment settings.

Towards Integrated Mobile Manipulation: A Holistic Approach

The Seamless Planning for Object Control (SPOC) framework represents a significant advancement in robotics by unifying navigation and manipulation into a cohesive system. Traditionally, these functions have been treated as separate problems, requiring cumbersome switching between different control algorithms. SPOC, however, allows a robot to plan for both movement and interaction with objects simultaneously, enabling it to navigate towards an object while concurrently figuring out how to grasp or manipulate it. This integrated approach avoids the pitfalls of sequential planning-where a robot might navigate perfectly to a location only to find it cannot physically reach or interact with the desired object-and allows for more fluid, efficient, and robust performance in complex environments. By treating manipulation as an integral part of the navigation process, SPOC paves the way for robots capable of truly intelligent and adaptable behavior.

Conventional robotic navigation often confines movement to pre-defined coordinates, limiting interaction with the world to a purely geometric plane. However, object-centric navigation represents a paradigm shift, allowing robots to perceive and move in relation to meaningful objects within their environment. Instead of simply traveling from point A to point B, a robot employing this approach can locate and approach a specific chair, circumvent an obstacle by moving around a table, or follow a person carrying a designated item. This capability moves beyond basic locomotion, enabling robots to perform complex tasks requiring understanding of object affordances and relationships – effectively allowing them to ‘reason’ about their surroundings and navigate with a purpose beyond simple path-following. The implications extend to dynamic environments where objects move and change, as the robot maintains awareness of these objects as reference points, rather than relying on static map data.

The quadrupedal robot, Boston Dynamics Spot, serves as a compelling example of how integrated mobile manipulation can move beyond laboratory settings and function effectively in unpredictable, real-world scenarios. This platform isn’t simply navigating from point A to point B; it’s demonstrating the ability to perceive its surroundings, plan complex actions involving both locomotion and manipulation, and adapt to unforeseen obstacles or changes. Spot’s capabilities – from autonomously inspecting industrial sites and performing remote data collection to assisting in construction and even delivering items – highlight the practical benefits of combining navigation with the ability to physically interact with the environment. These demonstrations showcase not just robotic agility, but a crucial step towards robots becoming truly useful collaborators in dynamic spaces, capable of responding to challenges and executing tasks previously requiring human intervention.

Demonstrating strong generalization, our object-centric navigation strategy achieves a 75% success rate when applied to unseen scenarios and object instances.

The research demonstrates a commitment to streamlined functionality, echoing Donald Davies’ observation that, “Simplicity is a prerequisite for reliability.” This pursuit of elegant solutions is evident in the framework’s reliance on RGB-only data for last-meter navigation. By centering the approach on object-centric representations and visual grounding, the system avoids unnecessary complexity. It’s a testament to how focusing on core elements-in this case, visual understanding of objects-can yield a robust and adaptable system, mirroring the principle that structure dictates behavior. The work embodies a holistic view, acknowledging that a successful navigation system isn’t merely about reaching a destination, but about understanding the environment and its components.

Where the Path Leads

The demonstrated capacity to navigate the final meter using solely RGB data, and indeed to ground actions within an object-centric framework, is not merely a technical achievement. It exposes a deeper truth: that robust manipulation hinges not on exquisite sensing, but on a coherent understanding of affordances. The current work, while effective, still operates within curated environments. The inevitable progression lies in confronting the chaos of the genuinely unknown – cluttered spaces, dynamic obstacles, and the sheer variability of real-world objects. The limitations of segmentation-reliant approaches will become starker as complexity increases; a system that requires precise object boundaries will inevitably fail where those boundaries are ambiguous or ephemeral.

Future work should focus less on perfecting perception and more on developing architectures capable of graceful degradation. A system built on probabilistic reasoning, anticipating uncertainty rather than reacting to it, will prove far more resilient. The question is not whether a robot can see an object, but whether it can reason about its interaction with that object, even with incomplete information. This necessitates a shift toward models that prioritize relational understanding – the spatial and functional connections between objects – over pixel-level accuracy.

The elegance of any such system will not be apparent in its successes, but in its failures. Good architecture is invisible until it breaks, and only then is the true cost of decisions visible.

Original article: https://arxiv.org/pdf/2512.11173.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/