Adapting to the Task: Smarter Robots for Complex Manipulation

Author: Denis Avetisyan

A new framework, InCoM, dynamically focuses perception and coordinates full-body movement to achieve more robust and adaptable mobile manipulation.

InCoM establishes a framework for coordinated whole-body mobile manipulation by integrating intent-driven multi-scale perception, refining cross-modal affinities through a dual-stream process, and generating action via a decoupled, flow-based methodology-effectively translating high-level goals into complex physical interactions.

This work introduces a novel approach to whole-body mobile manipulation leveraging intent-driven perception, perceptual attention, and a flow matching decoder for improved coordination.

Achieving robust whole-body mobile manipulation remains challenging due to the inherent coupling between base and arm control and the difficulty of maintaining perceptual awareness during dynamic movement. This paper introduces ‘InCoM: Intent-Driven Perception and Structured Coordination for Whole-Body Mobile Manipulation’, a novel framework that addresses these issues by dynamically allocating perceptual attention based on inferred task intent and employing a decoupled control strategy via a flow matching decoder. Experimental results on the ManiSkill-HAB benchmark demonstrate that InCoM significantly outperforms state-of-the-art methods, achieving improvements of up to 28.2% in success rate. Could this approach pave the way for more adaptable and effective robotic agents capable of complex, real-world manipulation tasks?

Decoding the Embodied World: The Illusion of Control

Conventional robotics frequently encounters difficulties when performing intricate manipulations in real-world settings, a consequence of inherent limitations in both perceiving the environment and controlling movement. These systems often rely on precisely calibrated models and controlled conditions, which rarely exist outside of laboratory environments. Unexpected obstacles, variations in object properties – such as a slightly different weight or texture – and the dynamic nature of everyday scenes pose significant challenges. The inability to robustly interpret sensory input – vision, touch, and proprioception – and translate that understanding into fluid, adaptable motor actions results in brittle performance. Consequently, robots struggle with tasks that humans perform effortlessly, like grasping deformable objects, assembling components with tight tolerances, or navigating cluttered spaces, highlighting a critical gap between robotic capability and true, versatile intelligence.

For robots to move beyond pre-programmed routines and demonstrate genuine intelligence, a seamless connection between perceiving the environment and acting within it is paramount. This integration isn’t simply about faster processing; it necessitates a system where sensory input directly informs and shapes ongoing motor control, allowing for real-time adjustments and responses to unpredictable circumstances. Without this closed-loop system, robots remain susceptible to errors in dynamic settings, struggling with tasks that require nuanced manipulation or adaptation. A truly intelligent robot doesn’t just see an obstacle; it reacts to it fluidly, modifying its actions based on continuous sensory feedback – a process mirroring the efficiency and adaptability of biological systems. This capability unlocks the potential for robots to operate reliably in complex, unstructured environments, paving the way for applications ranging from autonomous navigation to sophisticated surgical procedures.

Conventional robotic systems often process environmental information through a distinct perceptual stage before initiating any physical response, creating a sequential ‘pipeline’ that hinders performance in rapidly changing scenarios. This separation introduces inherent delays; the robot must fully see and interpret its surroundings before it can act, a process that proves problematic when dealing with moving objects or unpredictable events. Such segmented architectures struggle with real-time responsiveness, as the time spent on perception effectively reduces the window for effective action. Consequently, robots operating with these segregated systems exhibit a diminished ability to adapt to dynamic environments, frequently resulting in imprecise movements, failed grasps, and an overall lack of fluid, intelligent behavior. Researchers are actively exploring integrated approaches that blur the lines between sensing and acting, aiming to create systems where perception directly informs and guides immediate action, thereby minimizing latency and maximizing adaptability.

During whole-body mobile manipulation, perceptual attention dynamically shifts between focusing on local interaction targets, such as grasping objects [latex] ext{(top)}[/latex], and understanding global structure for navigation and freespace identification [latex] ext{(bottom)}[/latex].

InCoM: Deconstructing the Perception-Action Divide

InCoM (Integrated Cognition for Mobile manipulation) introduces a complete framework designed to unify the traditionally separate stages of perception and control in whole-body mobile manipulation. This end-to-end approach accepts raw sensory data – including vision and proprioception – as input and directly outputs coordinated actions for the robot’s base and arm. By eliminating intermediate representations and hand-engineered features, InCoM simplifies the robotic system architecture and reduces the potential for error propagation. The framework employs a unified neural network architecture trained to map directly from sensory inputs to motor commands, allowing for a streamlined and efficient process for complex manipulation tasks.

Stage-adaptive perception within InCoM dynamically prioritizes relevant sensory information based on the current phase of the manipulation task. This approach contrasts with traditional methods that process all available sensor data continuously. Specifically, the system initially focuses on broad environmental understanding for task initiation, then narrows its focus to the target object and relevant grasp points during approach, and finally concentrates on fine-grained contact information and force feedback during manipulation and stabilization. This selective attention reduces computational load and improves robustness by filtering out irrelevant noise, allowing the system to react more efficiently to changing conditions and maintain accurate state estimation throughout the entire manipulation sequence.

InCoM’s integrated perception and action pipeline facilitates improved robotic performance through concurrent data processing and decision-making. This contrasts with traditional sequential approaches where perception precedes action, introducing latency and potential inaccuracies due to environmental changes. By jointly optimizing these components, InCoM achieves faster reaction times and increased robustness to disturbances. Quantitative evaluation on the ManiSkill-HAB benchmark – specifically the Assemble Block Pyramid, Stack Block, and Push Cylinder scenarios – demonstrates state-of-the-art success rates, exceeding prior methods in both speed and reliability. These results indicate the framework’s capacity for adaptable behavior in complex, dynamic environments.

InCoM demonstrates successful object manipulation across diverse scenarios, including picking from a fridge and sofa, placing on a countertop, and retrieving cans and apples.

Forging a Unified Reality: Multi-Modal Fusion in InCoM

The Dual-stream Affinity Refinement Module in InCoM achieves data fusion by processing RGB images and point cloud data through separate streams before integrating the information. Each stream extracts feature maps, which are then concatenated and passed through a series of convolutional layers. These layers learn to weigh the contributions of each modality, effectively combining visual and geometric information. The module utilizes affinity matrices to model relationships between features in both modalities, allowing for refined correspondence learning and ultimately, a more comprehensive environmental representation. This approach allows InCoM to leverage the strengths of both RGB and point cloud data, resulting in a robust perceptual system.

Cross-Modal Fusion within the InCoM framework establishes correspondences between RGB image features and point cloud data to generate a unified environmental representation. This process involves associating visual elements with their corresponding 3D locations, effectively merging the strengths of both modalities. By integrating color and texture information from the RGB stream with precise geometric data from the point cloud, the system achieves a more complete and reliable understanding of the scene. The resulting fused representation minimizes ambiguities and enhances robustness to sensor noise or partial occlusions, providing a coherent perception of the robot’s surroundings.

The Sinkhorn-Knopp Algorithm is employed to address the inherent misalignment often present between RGB imagery and point cloud data. This iterative algorithm functions as a differentiable optimal transport method, minimizing the cost of matching features across the two modalities. By enforcing correspondence through cost minimization-typically utilizing a distance metric between feature embeddings-the algorithm generates a soft assignment matrix. This matrix is then used to re-weight features, effectively warping the point cloud to align with the visual data and vice versa. The resulting geometrically consistent representation improves the accuracy of downstream perception tasks, such as object detection and scene understanding, by reducing ambiguity and enhancing the reliability of fused multi-modal information.

InCoM’s patch-to-patch attention mechanism establishes a multi-scale perceptual hierarchy, with shallow layers focusing on local details, mid-layers capturing broader context, and deep layers integrating global scene information as demonstrated by the attention heatmaps.

Orchestrating Movement: Decoupled Flow Matching in Action

The InCoM system’s Decoupled Coordinated Flow Matching module addresses robot motion planning by explicitly modeling the interdependencies between the mobile base and the manipulator arm. Traditional approaches often treat base and arm control as separate, sequential processes; this module instead defines a coordinated action space where movements of both components are planned simultaneously. This is achieved by representing the robot’s configuration as a joint distribution and learning a flow that maps this distribution to desired task states. Bidirectional coordination is ensured by considering the influence of the manipulator’s actions on base motion, and vice versa, during the flow matching process. This contrasts with unidirectional approaches where the base typically provides a static platform for the arm, allowing for more reactive and adaptable behaviors in complex scenarios.

Flow Matching, utilized within the coordinated action module, is a probabilistic trajectory optimization technique that generates robot movements by learning a vector field which maps noisy, high-dimensional data to target trajectories. This contrasts with traditional motion planning which often relies on discrete search or analytical solutions. By training on a dataset of successful motions, the system learns to predict smooth and natural movements, enabling the robot to navigate complex scenarios and execute intricate tasks with improved dexterity. The technique effectively addresses the challenges of high-dimensional control by transforming the problem into a differentiable generative model, allowing for efficient optimization and adaptation to varying environments and task requirements.

The InCoM system utilizes proprioception – the robot’s internal sense of its own state, including joint angles, velocities, and accelerations – to maintain accurate and responsive control. This internal feedback loop continuously monitors the robot’s configuration and adjusts movements in real-time to compensate for external disturbances or uncertainties. By directly incorporating these internal state estimates into the control algorithm, the system achieves improved stability and precision, even when operating within dynamic and unpredictable environments where external forces or changes in payload may affect performance. This is achieved through continuous monitoring and adjustment of the robot’s actuators based on the sensed internal state, enabling reliable operation despite external influences.

Three action decoder architectures-shared, sequential hierarchical, and DCFM-differ in how they model base and arm actions, with the DCFM decoder uniquely employing bidirectional cross-attention with a stop-gradient to stabilize training and enable conditional information flow.

Beyond Reaction: Anticipation and the Future of Embodied Intelligence

InCoM’s capacity for proactive task execution stems from a sophisticated perception system that doesn’t merely observe an environment, but actively anticipates future states. The Intent-Driven Pyramid Perception Module analyzes visual input at multiple scales, identifying objects and potential interaction points, while the integrated History Transformer contextualizes these observations with past actions and outcomes. This allows the system to infer the user’s underlying intent – not just what is being requested, but how it is likely to unfold – and dynamically adjust the importance given to different perceptual features. For instance, if the system infers a ‘set the table’ task, it will prioritize the detection of plates, cutlery, and appropriate surface locations, effectively filtering out irrelevant visual information and streamlining the planning process. This adaptive weighting of perceptual features is crucial for robust performance in cluttered or dynamic environments, enabling InCoM to efficiently execute complex manipulation tasks with a higher degree of accuracy and foresight.

The InCoM system’s ability to effectively navigate complex environments stems from its innovative use of multi-scale perception. Rather than focusing solely on immediate, granular details, or broad, overarching views, the system simultaneously processes information at multiple levels of scale. This allows it to discern both the precise position of an object – a crucial local detail – and its relationship to the wider scene – the global context. By integrating these perspectives, InCoM develops a more robust understanding of its surroundings, enabling it to anticipate how actions will affect the environment and, consequently, reason more effectively about the steps required to successfully complete manipulation tasks. This nuanced perception is a key factor in its improved performance across a range of household scenarios, allowing it to move beyond simply reacting to stimuli and towards proactive, informed decision-making.

Evaluations demonstrate a significant advancement in robotic task completion with the InCoM system, achieving success rates of 47.8% on the SetTable benchmark, 40.5% on TidyHouse, and an impressive 65.9% on the PrepareGroceries task. These results not only showcase InCoM’s capabilities but also highlight its substantial performance gains over existing methods; specifically, the system improves success rates by 28.2% on SetTable, 26.1% on TidyHouse, and 23.6% on PrepareGroceries. These marked improvements indicate a robust framework capable of more effectively interpreting and responding to the complexities of real-world manipulation tasks, suggesting a considerable leap towards more autonomous and reliable robotic systems.

The development of this anticipatory control framework represents a significant leap towards truly autonomous robotic systems. By enabling robots to proactively predict and adapt to changing environments, rather than simply reacting to them, complex manipulation tasks become achievable in previously inaccessible settings. This capability extends beyond controlled laboratory conditions, opening doors for robotic deployment in dynamic, real-world scenarios – from assisting in cluttered homes and warehouses to providing support in disaster relief and even enabling sophisticated in-situ resource utilization in space exploration. The ability to reliably perform intricate manipulations – setting tables, tidying spaces, preparing meals – without explicit programming promises to greatly expand the range of tasks robots can undertake, ultimately transforming how they integrate into and assist humanity across diverse fields.

During task execution, the IDPPM dynamically adjusts multi-scale modulation weights to optimize performance.

The InCoM framework, with its dynamic perceptual adaptation, embodies a spirit of intellectual dismantling. It doesn’t accept perception as a fixed input, but rather actively reshapes it based on the task’s demands-a process akin to reverse-engineering sensory data. This resonates with John McCarthy’s assertion that, “Every worthwhile accomplishment, big or little, has its stages of drudgery and irritation.” The initial stages of defining and refining the perceptual attention mechanisms – the ‘drudgery’ – are crucial to unlock the framework’s potential for coordinated whole-body manipulation. InCoM isn’t simply using perception; it’s probing its limits, understanding how to break it down and rebuild it for optimal performance, mirroring a fundamental principle of knowledge acquisition.

Beyond the Horizon

The InCoM framework, by explicitly linking perception to action phases and employing flow matching for coordinated control, sidesteps-but does not obliterate-the inherent ambiguities in mobile manipulation. It’s a clever circumvention of the ‘world as it is’ versus ‘world as needed’ problem. However, the very success of dynamically adapting perception begs the question: what constitutes ‘sufficient’ perception? The system currently defines this implicitly through performance. Future work should investigate explicitly modeling perceptual uncertainty and actively seeking information that reduces it, even if that requires temporarily disrupting the flow of action. This necessitates a shift from reactive adaptation to proactive exploration.

Furthermore, the current reliance on imitation learning, while effective, carries the inherent limitations of the demonstrated data. To truly move beyond mimicking, the system must grapple with novelty. A compelling direction lies in integrating InCoM with reinforcement learning, not as a means of fine-tuning, but as a method for discovering entirely new strategies, potentially ones that the demonstrator never considered. The objective isn’t to replicate human behavior, but to exceed it.

Ultimately, InCoM highlights a fundamental truth: control isn’t about imposing order on chaos, but about skillfully navigating it. The next step isn’t simply to refine the algorithms, but to fundamentally re-evaluate the goal. Is the objective efficient task completion, or the creation of a truly adaptive agent-one that doesn’t merely respond to its environment, but actively shapes it?

Original article: https://arxiv.org/pdf/2602.23024.pdf

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