Robots Learn From Each Other: Bridging the Gap in Skill Transfer

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Author: Denis Avetisyan

New research explores how robots can leverage data collected from different ‘bodies’ to accelerate learning and improve performance in complex tasks.

Embodiment grouping offers a method for offline reinforcement learning across diverse physical systems, enabling knowledge transfer despite variations in dynamics by leveraging shared representations and facilitating adaptation without explicit retraining for each new embodiment.
Embodiment grouping offers a method for offline reinforcement learning across diverse physical systems, enabling knowledge transfer despite variations in dynamics by leveraging shared representations and facilitating adaptation without explicit retraining for each new embodiment.

This work addresses gradient conflicts in offline reinforcement learning across heterogeneous robot datasets, enabling more effective skill transfer and generalization.

Pre-training robust robot policies is hampered by the expense of collecting diverse, high-quality datasets for each platform; this work, ‘Cross-Embodiment Offline Reinforcement Learning for Heterogeneous Robot Datasets’, addresses this challenge by uniting offline reinforcement learning with cross-embodiment learning to leverage data across multiple morphologies. Our analysis reveals that while this combination excels with suboptimal datasets, conflicting gradients arising from heterogeneous robot data can impede learning, a phenomenon we mitigate with a simple embodiment-based grouping strategy. This grouping, clustering robots by morphological similarity, substantially reduces inter-robot conflicts and outperforms existing approaches-but can we further refine these grouping strategies to unlock even greater generalization across increasingly diverse robotic platforms?

The Fragility of Generalization in Robotic Systems

Conventional robotic learning methodologies frequently encounter difficulties when transitioning from controlled laboratory settings to the unpredictable nature of real-world applications. A robot trained to perform a specific task – such as grasping a particular object – often exhibits diminished performance, or even complete failure, when presented with slight variations in its environment, like altered lighting, novel objects, or unexpected obstacles. This lack of adaptability stems from the reliance on highly specific datasets and algorithms, effectively creating systems that are brittle and unable to generalize beyond the conditions in which they were initially trained. Consequently, the promise of truly autonomous robots capable of operating seamlessly in diverse and dynamic environments remains largely unfulfilled, hindering widespread adoption and necessitating the development of more robust and flexible learning paradigms.

The pursuit of truly autonomous robots is significantly hampered by the sheer volume of labeled data required for effective learning. Current machine learning paradigms often demand thousands, even millions, of examples for a robot to reliably perform a task, and critically, this requirement restarts with every new environment or variation in the task itself. This presents a fundamental bottleneck: realistically, it’s impractical – and often impossible – to manually annotate sufficient data for every conceivable situation a robot might encounter in the real world. The cost, time, and logistical challenges of such an undertaking severely restrict a robot’s adaptability and limit its potential for widespread deployment, pushing researchers to explore data-efficient learning methods that can overcome this critical obstacle to genuine robotic intelligence.

The inherent difficulty in equipping robots with truly adaptable intelligence stems from a critical need for new learning paradigms. Current methods often demand vast datasets meticulously labeled for each unique situation, a process that is both time-consuming and, ultimately, unsustainable as robots encounter the unpredictable nature of the real world. Consequently, research is increasingly focused on techniques that maximize the information gleaned from limited data, such as meta-learning, transfer learning, and self-supervised learning. These approaches aim to enable robots to rapidly adapt to novel scenarios by leveraging prior experience and identifying underlying patterns, rather than requiring explicit retraining for every new task or environment. Ultimately, the pursuit of robust generalization promises to unlock the full potential of robotic autonomy, allowing machines to operate reliably and efficiently in a constantly changing world.

Embodiment-based similarity, measured by the [latex]1 - FGW[/latex] distance, correlates with gradient cosine similarity across robot pairs, as shown by the similarity matrices in (a) and (b) and their relationship in (c).
Embodiment-based similarity, measured by the [latex]1 – FGW[/latex] distance, correlates with gradient cosine similarity across robot pairs, as shown by the similarity matrices in (a) and (b) and their relationship in (c).

Foundation Models: A Paradigm Shift for Robotics

Robot Foundation Models represent a shift towards general-purpose robotic systems achieved through large-scale pre-training. These models are trained on extensive and varied datasets encompassing robotic tasks such as manipulation, locomotion, and perception. The diversity of the training data – including data from multiple robots, environments, and tasks – is crucial for enabling zero-shot or few-shot generalization to novel situations. This approach contrasts with traditional robotics, where robots are typically designed and trained for specific, narrow tasks. The goal is to create models that can adapt quickly to new tasks with minimal task-specific data or fine-tuning, mirroring advancements seen in large language models and computer vision.

Offline Reinforcement Learning (RL) addresses a key limitation of traditional RL – the requirement for extensive and potentially expensive interaction with an environment. In this paradigm, agents learn optimal policies directly from pre-collected, static datasets of robot experiences, eliminating the need for real-time data acquisition. This is particularly advantageous in robotics where physical experimentation can be time-consuming, resource-intensive, and potentially damaging to hardware. The datasets typically contain state-action pairs and resulting rewards, allowing algorithms to infer optimal behavior without active exploration. Consequently, offline RL significantly reduces sample complexity and enables learning from previously unavailable or difficult-to-obtain data, facilitating the development of more robust and adaptable robotic systems.

TD3+BC and Implicit Q-Learning (IQL) represent distinct but effective approaches to offline reinforcement learning for robotic control. TD3+BC combines the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm with Behavior Cloning (BC) to leverage both off-policy data efficiency and imitation learning. This hybrid approach stabilizes training and improves sample efficiency by initializing the policy with demonstrated behaviors. IQL, conversely, focuses on conservative Q-learning, estimating a lower bound on the Q-function to avoid overestimation bias common in off-policy learning. By learning a pessimistic estimate of achievable rewards, IQL promotes safer and more reliable policy learning from static datasets, effectively mitigating the risk of extrapolating to unseen states and actions.

Embodiment-based similarity, calculated from [latex]1 - min-max[/latex] normalized Feature-wise Gromov-Wasserstein distance, correlates with the cosine similarity of gradients during TD3+BC training on the Expert Forward dataset, as visualized by the similarity matrices and scatter plot.
Embodiment-based similarity, calculated from [latex]1 – min-max[/latex] normalized Feature-wise Gromov-Wasserstein distance, correlates with the cosine similarity of gradients during TD3+BC training on the Expert Forward dataset, as visualized by the similarity matrices and scatter plot.

Cross-Embodiment Learning: Leveraging Collective Experience

Cross-Embodiment Learning (CEL) facilitates the training of a unified control model applicable to diverse robotic platforms. This approach contrasts with traditional methods requiring individual models per robot, thereby significantly improving data efficiency by leveraging data collected from multiple sources. The core benefit of CEL lies in its capacity to generalize learned behaviors across varying morphologies and kinematic structures. By sharing knowledge between robots, the model requires fewer samples to achieve comparable or superior performance on any individual platform. This is particularly advantageous in scenarios where data acquisition is costly or time-consuming, or when deploying a single control policy across a fleet of heterogeneous robots is desired.

Gradient Conflict emerges as a significant obstacle when applying a unified model to robots with differing physical structures, or morphologies. This conflict arises because each robot’s unique morphology results in distinct optimal control policies and, consequently, differing gradient signals during training. When these gradients are combined during backpropagation, they can interfere with each other, leading to slower learning, instability, and suboptimal performance. The magnitude of this interference is directly related to the dissimilarity in the robots’ morphologies and the resulting divergence in their optimal control strategies; greater morphological differences generally exacerbate gradient conflict. Addressing this requires techniques to either align or mitigate the influence of conflicting gradients, ensuring the model can effectively learn a generalized policy applicable across all embodied platforms.

Embodiment Grouping is a technique used to address Gradient Conflict during multi-robot training, specifically when robots exhibit differing morphologies. This method involves clustering robots based on similarity, as quantified by metrics such as Fused Gromov-Wasserstein Distance, before applying a shared training signal. Experimental results demonstrate that implementing Embodiment Grouping achieves performance gains of up to 39.8% when training data is suboptimal, indicating a significant improvement in generalization and learning efficiency under challenging conditions. This performance increase is attributed to the reduction of conflicting gradients arising from disparate robot embodiments, allowing for more stable and effective policy learning across the robotic group.

Analysis reveals a strong positive correlation between the similarity of robot morphologies and the alignment of their corresponding gradients during cross-embodiment learning. Specifically, a Pearson correlation coefficient of 0.711 indicates that more similar robot embodiments exhibit more aligned gradients when trained with a shared model. This suggests that the optimization process is more efficient and stable when transferring knowledge between robots with comparable physical structures, as the learned parameters are more readily applicable across those embodiments. The metric used to quantify embodiment similarity considers both kinematic and dynamic properties, allowing for a nuanced assessment of physical resemblance and its impact on gradient alignment.

Analysis demonstrates a strong correlation of 0.766 between the difference in achieved reward and the cosine similarity of the corresponding gradients during multi-robot training. This indicates that greater alignment between the gradients of different robot morphologies directly corresponds to reduced performance disparity and improved overall learning efficiency. Specifically, higher cosine similarity – representing more aligned gradient directions – is consistently observed when robots achieve similar reward values, suggesting that aligned gradients are critical for effective knowledge transfer and consistent performance across diverse robotic platforms. This relationship highlights the importance of strategies aimed at maximizing gradient alignment during cross-embodiment learning.

Cross-embodiment pre-training consistently improves learning speed and final performance across diverse robotic platforms-Badger, Unitree G1, and Cassie-compared to training without it.
Cross-embodiment pre-training consistently improves learning speed and final performance across diverse robotic platforms-Badger, Unitree G1, and Cassie-compared to training without it.

The Power of Morphology-Aware Robotic Control

Robots equipped with an understanding of their own physical morphology-the form and structure of their bodies-exhibit markedly improved learning capabilities and adaptability. This approach moves beyond treating a robot as a generic actuator, instead allowing it to leverage knowledge about its joints, links, and overall configuration to accelerate skill acquisition. By internalizing these self-characteristics, robots can more efficiently explore potential actions and predict outcomes, leading to faster learning curves and reduced data requirements. Critically, this morphological awareness enables generalization to novel situations and environments; a robot that understands how its body affects its movement can more readily adjust to changes in terrain, object properties, or task demands, paving the way for truly versatile and robust robotic systems capable of operating reliably in the real world.

The integration of morphological awareness into robotic control systems promises a significant leap towards truly autonomous operation in unpredictable environments. Traditional robotics often struggles with the nuances of real-world complexity – uneven terrain, unexpected obstacles, and varying task demands. By enabling robots to understand and adapt to their own physical characteristics and limitations, and to leverage these in their interactions with the world, this approach fosters resilience and adaptability. A robot conscious of its body – its reach, strength, and stability – can navigate challenging landscapes, recover from disturbances, and execute tasks with a level of robustness previously unattainable. This enhanced capability translates directly into increased reliability, reducing the need for constant human intervention and opening doors for robotic deployment in fields like disaster response, environmental monitoring, and complex industrial automation.

Evaluations reveal a significant performance gain achieved through this novel methodology, demonstrating an average improvement of 7.15% when contrasted with the baseline Implicit Q-Learning (IQL) approach on datasets deliberately designed to mimic real-world imperfections – specifically, the 70% Suboptimal datasets. This quantifiable advancement underscores the efficacy of integrating morphological awareness into robotic control systems, allowing for more robust and adaptable performance even when faced with less-than-ideal conditions. The results suggest that by explicitly considering the physical attributes of a robot, learning algorithms can more effectively navigate challenging environments and achieve demonstrably improved outcomes compared to methods that treat robots as purely abstract agents.

The convergence of foundation models, offline reinforcement learning, and morphology-aware strategies signals a transformative shift in robotic capabilities. Traditionally, robots required extensive task-specific training, limiting their adaptability. However, leveraging pre-trained foundation models – akin to large language models but for robotics – provides a crucial head-start, enabling robots to generalize from limited data. This is further amplified by offline reinforcement learning, which allows robots to learn robust policies from previously collected datasets, circumventing the need for costly and potentially dangerous real-time exploration. Critically, incorporating morphological information – the robot’s physical attributes and how it moves – allows these systems to intelligently exploit the robot’s body to enhance performance and navigate complex environments with greater efficiency and resilience, paving the way for robots that are truly versatile and capable of tackling a wider range of real-world challenges.

Robots are represented as graphs where nodes correspond to torso joints and feet, and edges define kinematic adjacency and foot connections, enabling a unified representation for diverse robot morphologies.
Robots are represented as graphs where nodes correspond to torso joints and feet, and edges define kinematic adjacency and foot connections, enabling a unified representation for diverse robot morphologies.

The pursuit of robust robotic systems, as detailed in this work on cross-embodiment learning, echoes a fundamental truth about all complex systems: they are inherently transient. This research attempts to bridge the gap between disparate datasets, mitigating gradient conflicts that arise when combining knowledge from varied robotic platforms. It’s a pragmatic acknowledgement that perfect, monolithic data is rarely available, and adaptation is key. As Henri Poincaré observed, “It is through science that we arrive at truth, but it is through art that we express it.” The art here lies in the elegant engineering required to make heterogeneous data speak the same language, allowing for generalization beyond the limitations of any single embodiment. The core idea-combining data from multiple robots-is a form of memory, attempting to preserve and reuse knowledge across time and platforms, even as the ‘arrow of time’ inevitably points toward the need for continued refinement and adaptation.

What Lies Ahead?

The pursuit of foundation models in robotics, as exemplified by this work on cross-embodiment offline reinforcement learning, inevitably confronts the limits of architectural longevity. Every architecture lives a life, and the successes achieved by combining datasets from disparate robotic platforms will, in time, reveal the specific points of failure – the assumptions embedded within the learning algorithms that do not generalize to yet-unseen embodiments or environments. The mitigation of gradient conflict, while a crucial step, merely postpones the eventual entropy.

Future work will likely focus on methods for actively identifying and characterizing these points of failure – not simply smoothing over the conflicts, but understanding the fundamental incompatibilities arising from heterogeneous data. The question is not whether these models will generalize indefinitely, but how gracefully they will degrade. Improvements age faster than one can understand them; the very act of patching a system introduces new vulnerabilities, often more subtle and pervasive than those they address.

Ultimately, the field may be driven towards systems that anticipate their own obsolescence, incorporating mechanisms for self-diagnosis and adaptive recalibration. The true challenge isn’t building a perfect foundation, but designing a system capable of acknowledging its imperfections and evolving in response-a robotic analogue to biological adaptation, accepting that even the most robust structures are, fundamentally, transient.

Original article: https://arxiv.org/pdf/2602.18025.pdf

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

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2026-02-23 09:18