One Policy to Rule Them All: Generalized Robot Learning with 3D Scene Understanding

Author: Denis Avetisyan

Researchers have developed a new framework that allows robots to seamlessly adapt to different hardware and sensors, dramatically simplifying the deployment of complex manipulation skills.

The proposed framework acquires manipulation skills through episodic demonstrations, trains a visuomotor policy utilizing the GenDP pipeline, and subsequently validates its performance on designated hardware via the D³Fields feature extraction pipeline.

This work introduces a flexible, diffusion policy-based approach leveraging 3D semantic fields (D³Fields) for cross-robot and sensor compatibility in robot manipulation tasks.

Achieving robust generalization in robotic manipulation remains challenging due to the variability of robot hardware and sensing modalities. This limitation is addressed in ‘A Flexible Field-Based Policy Learning Framework for Diverse Robotic Systems and Sensors’, which introduces a visuomotor learning framework integrating diffusion policies with 3D semantic scene representations. The resulting system demonstrates successful grasp-and-lift performance-achieving 80% success after only 100 demonstrations-across both UR5 + Azure Kinect and ALOHA + RealSense configurations. Does this adaptable architecture represent a viable path toward scalable, real-world robotic deployments and truly generalized skill transfer?

The Perceptual Bottleneck: Limitations of Current Robotic Systems

Robotic systems, despite advancements in individual task performance, frequently falter when confronted with even slight deviations from their training environment. This limitation stems from a reliance on narrowly defined parameters and a difficulty in abstracting learned behaviors to new, unseen scenarios. A robot proficient at assembling a product on one conveyor belt may struggle significantly when presented with a slightly different arrangement, altered lighting, or an object with minor variations. This lack of generalization isn’t a matter of mechanical capability, but rather a deficit in the system’s ability to interpret the world flexibly and adapt pre-programmed actions accordingly; a seemingly simple task for a human, but a substantial hurdle for current robotic intelligence.

Robotic systems frequently falter when transitioning from controlled laboratory environments to the complexities of real-world scenarios, largely due to deficiencies in interpreting three-dimensional space. Current control policies often operate on limited, two-dimensional data or rely on painstakingly crafted environmental models, hindering adaptability. A truly robust robot requires not simply seeing an environment, but comprehensively understanding its geometry, object affordances, and potential interactions. This demands a tight integration between perception systems – those responsible for 3D reconstruction and object recognition – and the control algorithms that dictate movement and manipulation. Without this cohesive link, robots struggle to predict the consequences of their actions, leading to errors in grasping, navigation, and overall task completion. Advancements in areas like neural radiance fields and differentiable rendering offer promising pathways towards building systems capable of both perceiving and acting within a fully realized 3D world, enabling a more seamless and intuitive interaction with the environment.

Many contemporary robotic systems face limitations due to their dependence on meticulously designed features or the necessity for vast quantities of labeled data for each new task. This reliance presents a significant bottleneck, hindering a robot’s ability to adapt to unfamiliar environments or generalize learned skills. Hand-engineered features, while providing initial control, often prove brittle when confronted with the variability of the real world, requiring constant recalibration and limiting scalability. Conversely, data-hungry approaches, such as deep learning, demand extensive and costly annotation efforts, making deployment in dynamic or infrequently encountered scenarios impractical. This creates a critical need for methods that enable robots to learn more efficiently from limited data and leverage inherent scene understanding, rather than relying on pre-programmed solutions or exhaustive training regimens.

The robot successfully completes a grasp-and-lift task by approaching, grasping, and lifting the target object from the table.

GenDP: A Semantic Foundation for Robust Generalization

GenDP integrates 3D semantic fields directly into the imitation learning pipeline to enhance a robot’s understanding of its environment. This is achieved by representing the scene as a volumetric grid where each voxel contains semantic labels identifying objects and their properties. These semantic fields, derived from point cloud data, provide the robot with spatial and semantic context beyond simple geometric information. By incorporating this semantic understanding, the imitation learning process can learn policies that are more robust to variations in object appearance, pose, and scene layout, effectively bridging the gap between perception and action.

By integrating 3D semantic fields, the GenDP framework enables robots to interpret visual input and directly assess an object’s potential for interaction – its affordances – and the limitations governing those interactions, known as constraints. This process circumvents the need for pre-programmed knowledge of object properties; instead, the robot infers these characteristics from the observed point cloud data. Specifically, the system identifies features indicative of grasp points, stable configurations, and potential collision points directly from the visual input, allowing for adaptation to novel objects and scenarios without requiring explicit re-programming for each instance.

The GenDP framework utilizes PointNet++ to directly process point cloud data acquired from the robot’s environment, enabling feature extraction crucial for policy learning. This architecture allows the system to bypass the need for manual feature engineering and learn representations directly from raw sensory input. Across eight distinct robotic manipulation tasks involving novel object instances, the implementation of PointNet++ and the resulting semantic understanding yielded a significant performance increase, ranging from 20% to 93% improvement in task success rates compared to baseline methods. These tasks included variations in object pose, shape, and size, demonstrating the framework’s capacity for generalization to previously unseen scenarios.

D3Fields: Dynamic 3D Scene Understanding for Adaptive Robotics

D3Fields builds upon the GenDP framework by introducing 3D descriptor fields that are dynamic, semantic, and implicitly represented. These fields move beyond static scene geometry to incorporate time-varying information, allowing for representation of moving objects and changing environments. Semantic information is integrated, associating descriptive labels with specific regions of the 3D space – for example, identifying areas as “table”, “chair”, or “floor”. Implicit representation utilizes continuous functions to define the 3D scene, enabling compact storage and efficient querying compared to explicit voxel or mesh-based methods. This combination results in a more comprehensive and informative 3D scene representation suitable for robotic perception and manipulation tasks.

D3Fields leverages several advanced vision models to acquire robust feature extraction capabilities. Specifically, the Segment Anything Model (SAM) provides strong segmentation data, while Grounding-DINO and DINOv2 facilitate object detection and classification. These models are complemented by XMem, a video object segmentation model that enhances temporal consistency and tracking. By integrating these state-of-the-art architectures, D3Fields obtains detailed and reliable visual information crucial for dynamic 3D scene understanding in robotic applications.

D3Fields leverages the Feature Fusion for 3D Representations with Multi-view Consistency (F3RM) framework to create a consolidated 3D feature representation from existing 2D foundation models. F3RM achieves this by fusing features extracted from multiple viewpoints, enforcing consistency across these views to generate a unified 3D understanding of the scene. This process allows D3Fields to utilize the strengths of pre-trained 2D models – such as those capable of object detection or semantic segmentation – and extend their capabilities into the 3D domain without requiring extensive 3D training data. The resulting 3D feature representation captures both geometric and semantic information, facilitating downstream robotic tasks like scene understanding and manipulation.

Precise Data Acquisition: The Episodic Recording System

The Episodic Recording System is designed to capture time-synchronized data streams from multiple Azure Kinect depth cameras and a Universal Robots UR5 robotic arm. This system records RGB (color) and depth data from the Kinects, providing both visual texture and 3D spatial information. Simultaneously, it logs the UR5’s joint positions, velocities, and applied torques. This multi-modal data capture is crucial for applications requiring correlation between visual perception and robotic manipulation, such as robot learning, 3D reconstruction, and interactive teleoperation. Data is recorded in episodic segments, allowing for manageable data sizes and focused analysis of specific interaction sequences.

Multi-camera synchronization is achieved to ensure all RGB-D data streams are temporally aligned, a necessity for generating accurate 3D reconstructions. Discrepancies in timing between cameras introduce errors in point cloud registration and subsequent 3D modeling. The system employs hardware and software techniques to minimize these discrepancies, typically utilizing common clock sources and precise timestamping of each frame. Synchronization accuracy is crucial, with tolerances often measured in microseconds or milliseconds depending on the speed of robotic arm movements and the desired fidelity of the 3D model. Failure to properly synchronize data results in distorted or inaccurate representations of the captured scene.

The system architecture leverages ROS 2 for inter-process communication and device control, facilitating data exchange between Azure Kinect cameras, the UR5 robotic arm, and associated software components. Specifically, Gello software is integrated to provide SpaceMouse-based teleoperation capabilities, enabling users to intuitively control the UR5 arm and refine data collection parameters. This allows for precise positioning of the robotic arm during data acquisition, and facilitates manual adjustments to optimize the captured RGB-D streams for subsequent 3D reconstruction and analysis. The combination of ROS 2 and Gello/SpaceMouse provides a flexible and accurate means of controlling the data acquisition process.

The system achieves precise and responsive robotic arm control through integration with the Universal Robots UR5 utilizing the Real-Time Data Exchange (RTDE) interface. RTDE enables low-latency communication, allowing the framework to receive robot joint positions, velocities, and forces, as well as send commands for trajectory execution and force control. This direct communication pathway bypasses typical control loops, reducing delays to under 1ms and facilitating synchronized data acquisition between the robot and the Azure Kinect cameras. The framework leverages RTDE to both monitor robot state for accurate data association and to implement real-time adjustments to the UR5’s position during data collection, ensuring consistent and reliable data capture.

Towards Autonomous Systems: Broader Impact and Future Research

The development of robotic systems capable of navigating and interacting with unpredictable, real-world settings represents a significant leap forward in automation. This research establishes a foundation for robots that require substantially less human oversight, enabling them to function effectively in dynamic environments like warehouses, construction sites, or even domestic spaces. By focusing on adaptable 3D scene understanding and manipulation, these systems move beyond pre-programmed routines, allowing for on-the-fly adjustments to unforeseen obstacles or variations in object placement. The implications extend to numerous sectors, promising increased efficiency, reduced costs, and the potential to deploy robots in situations previously considered too challenging or hazardous for automated operation, ultimately fostering a new era of robotic autonomy.

The development of robust 3D scene understanding and generalizable manipulation skills promises significant advancements across several critical sectors. In logistics, robots equipped with these capabilities could autonomously navigate warehouses, identify and retrieve specific items, and streamline order fulfillment, even in dynamic and cluttered environments. Manufacturing processes stand to benefit from increased automation, with robots capable of handling a wider range of tasks – from assembly and quality control to packaging and material handling – leading to improved efficiency and reduced costs. Perhaps most profoundly, healthcare could see a transformation through robotic assistance in surgery, rehabilitation, and patient care, allowing for greater precision, personalized treatment, and support for an aging population – all facilitated by a robot’s ability to reliably perceive and interact with complex, real-world environments.

Ongoing research endeavors are directed toward significantly expanding the capabilities of this robotic framework to address increasingly intricate tasks. Current efforts prioritize the integration of this system with advanced learning-based planning algorithms, aiming to move beyond pre-defined scenarios and enable robots to autonomously formulate and execute complex manipulation strategies. This convergence of robust 3D perception and intelligent planning promises to unlock the potential for robots to operate with greater flexibility and adaptability in real-world settings, tackling challenges that demand not only skillful execution but also dynamic problem-solving and strategic decision-making. Ultimately, this integration seeks to create robotic systems capable of independent operation and proactive response to unforeseen circumstances, paving the way for widespread deployment in diverse and demanding applications.

The developed framework exhibits notable adaptability and performance across diverse robotic systems. Rigorous testing with both a UR5 robot paired with an Azure Kinect camera and an ALOHA robot utilizing a RealSense camera yielded impressive results; the system achieved success rates of 80% and 90%, respectively, on a foundational grasp-and-lift task. Beyond this specific challenge, the framework demonstrated a substantial average performance improvement of 46.9% when evaluated across a suite of twelve distinct tasks encompassing four widely recognized manipulation benchmarks, highlighting its generalizability and potential for broad application in robotic manipulation research and development.

This modular workcell framework enables flexible integration of diverse robots, control interfaces, and camera systems to create adaptable automation solutions.

The presented framework’s adaptability across diverse robotic systems and sensors mirrors a commitment to foundational principles. It isn’t merely about achieving functional results, but establishing a robust, provable system. As Donald Knuth observed, “Premature optimization is the root of all evil.” This holds true here; the focus isn’t simply on maximizing performance on a single configuration, but on building a generalized policy capable of consistent, correct operation across varying hardware. The framework’s use of D³Fields, creating a semantic representation of the environment, establishes invariants crucial for reliable manipulation, echoing the need for rigorous mathematical underpinnings in algorithm design. The system’s success across UR5/Azure Kinect and ALOHA/RealSense setups demonstrates a commitment to correctness, not just convenience.

What’s Next?

The demonstrated adaptability of this diffusion-based policy framework, while promising, merely shifts the locus of the problem, rather than solving it. Success across disparate robotic and sensor configurations does not address the fundamental issue of generalization itself. The current paradigm relies on learned mappings between sensory input and motor control – elegant, perhaps, but ultimately brittle. A truly robust system would require a deductive approach, one grounded in the physics of manipulation and object interaction, not inductive leaps from observed data. Reproducibility, predictably, remains paramount; a policy that fails to yield identical results given identical initial conditions is, at best, an approximation of control.

Future work must confront the inherent limitations of purely data-driven methodologies. The framework’s reliance on 3D semantic fields, while effective for the present task set, introduces another layer of abstraction susceptible to noise and perceptual error. One anticipates that a more principled approach-perhaps leveraging symbolic reasoning or formal verification-will be necessary to achieve genuinely reliable robotic manipulation. The question is not merely whether a robot can perform a task, but whether its actions are demonstrably correct.

The pursuit of cross-robot compatibility is laudable, yet risks becoming a distraction. The true challenge lies not in adapting policies to different hardware, but in developing policies that are independent of it. A universal manipulation policy, derived from first principles, would render such adaptations entirely unnecessary. Until then, the field remains bound to a cycle of incremental improvements, forever chasing an elusive ideal of true robotic intelligence.

Original article: https://arxiv.org/pdf/2512.19148.pdf

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