Robots Learn to See, Feel, and Act: Introducing RoboMIND 2.0

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Author: Denis Avetisyan

A new large-scale dataset empowers robots to perform complex manipulation tasks with improved generalization and robustness.

RoboMIND 2.0 is a multimodal dataset designed to advance research in embodied artificial intelligence and sim-to-real transfer for robotic manipulation.

Despite advances in data-driven robotics, current imitation learning approaches struggle with generalizing to complex, real-world manipulation tasks. To address this, we introduce RoboMIND 2.0: A Multimodal, Bimanual Mobile Manipulation Dataset for Generalizable Embodied Intelligence, a large-scale collection of over 310K trajectories-including tactile and mobile manipulation data-captured across diverse robot embodiments and tasks. This dataset, coupled with a simulated counterpart and a novel hierarchical reinforcement learning framework, enables the training of robust and generalizable robot policies capable of interpreting natural language instructions. Will this richer data and integrated approach unlock a new era of adaptable and intelligent robotic assistants?

The Illusion of Control: Why Robots Struggle in the Real World

Historically, robotic control systems have been painstakingly designed for narrowly defined tasks, demanding precise calibration and predictable environments. This approach, while capable of high performance under ideal conditions, proves brittle when confronted with the inevitable uncertainties of the real world. Each new task or even slight variation in the environment often necessitates a complete redesign and recalibration of the robot’s control algorithms. This reliance on meticulously engineered solutions limits a robot’s ability to generalize its skills, hindering its deployment in dynamic, unstructured settings where adaptability is paramount. The limitations of this traditional paradigm are increasingly apparent as robotics ventures beyond the controlled confines of factories and into more complex, real-world applications.

Robotic systems, despite advances in controlled laboratory settings, consistently encounter challenges when deployed in unstructured, real-world environments. This difficulty stems from the inherent variability of these spaces – unpredictable lighting, constantly shifting objects, and the dynamic nature of interactions all contribute to performance degradation. Current control algorithms, often designed for static conditions, struggle to generalize beyond their training parameters, leading to errors in perception, planning, and execution. Consequently, the widespread adoption of robots in domains like home assistance, search and rescue, and agriculture remains limited; the gap between controlled performance and robust real-world operation necessitates innovative approaches to perception and control that prioritize adaptability and resilience to unforeseen circumstances.

Mimicking the Mind: A Hierarchical Architecture for Robotic Intelligence

MIND2 is a robotic architecture modeled after the hierarchical organization of the brain, specifically incorporating two primary components: MIND2-VLM and MIND2-VLA. MIND2-VLM functions as the high-level planning module, responsible for task decomposition and strategic decision-making. Conversely, MIND2-VLA operates as the low-level action execution module, directly controlling actuators and managing real-time responses. This division of labor is intended to mirror the functional separation observed between the cerebral cortex and the cerebellum in biological systems, enabling a more efficient and robust control system for robotic applications.

The MIND2 architecture functionally separates robotic control into two distinct modules: the MIND2-VLM and the MIND2-VLA. The MIND2-VLM operates as a task planner, responsible for defining sequences of actions necessary to achieve broader goals. Conversely, the MIND2-VLA functions as an action executor, directly controlling the robot’s actuators to perform these defined actions. This division of labor allows for efficient operation by offloading detailed motor control from the planning module, and contributes to robustness through compartmentalization; failures within the VLA are less likely to disrupt high-level task planning performed by the VLM, and vice versa.

Learning Through Observation: Training the Robotic ‘Cerebellum’

The MIND2-VLA utilizes ImplicitQLearning, an offline reinforcement learning approach, to acquire manipulation skills. Unlike traditional reinforcement learning requiring active environmental interaction, ImplicitQLearning operates on pre-collected datasets, termed ‘offline’ learning. This technique distinguishes itself by learning from both successful and failed trajectories within the dataset; analyzing failures provides crucial information for refining the agent’s understanding of suboptimal actions and avoiding them in the future. The algorithm infers a reward function directly from the observed behavior in the dataset, allowing the VLA to learn a policy without explicit reward engineering. This is achieved through a behavioral cloning framework combined with a conservative Q-learning objective, which prioritizes actions similar to those observed in the dataset and penalizes actions with high uncertainty.

The VLA’s ability to generalize and perform complex manipulations is directly attributable to its training on the RoboMIND2.0 dataset, which comprises 310,000 trajectories. These trajectories were generated from six distinct robotic embodiments performing 759 unique tasks, utilizing a library of 1139 different objects. This extensive and varied dataset allows the VLA to develop a robust understanding of manipulation dynamics, enabling successful performance even in scenarios not explicitly represented within the training data. The diversity of robotic morphologies and task configurations contributes to the VLA’s adaptability and facilitates transfer learning to novel robotic platforms and object combinations.

The incorporation of tactile sensing data significantly improves the precision and robustness of the Visuo-Locomotion Agent (VLA), particularly in manipulation tasks requiring precise force application. This enhancement is achieved by providing the VLA with real-time feedback regarding contact forces and object properties, allowing for adjustments during task execution. Experimental results demonstrate that tactile sensing demonstrably improves performance in contact-rich manipulation scenarios, enabling more reliable grasping, assembly, and in-hand manipulation of objects with varying shapes, sizes, and fragility. The system utilizes tactile feedback to refine motor control, reducing instances of slippage, collisions, and task failure, and increasing overall success rates in complex manipulation tasks.

Bridging the Divide: Real-World Impact and Future Trajectories

The RoboMIND2.0 dataset represents a significant advancement in robotic research by directly addressing the challenge of transferring learned behaviors from simulation to the physical world – a process known as SimToReal transfer. This comprehensive resource comprises a diverse collection of robot trajectories, sensor data, and environmental variations, meticulously curated to enable robust policy learning. By providing a rich and realistic training ground, RoboMIND2.0 allows algorithms to generalize more effectively when deployed on actual robotic hardware, bypassing the limitations of solely training in either simulated or real-world environments. The dataset’s scale and diversity empower researchers to develop and validate algorithms capable of navigating the complexities of real-world scenarios, ultimately accelerating the creation of adaptable and versatile robots.

The integration of DigitalTwin technology represents a significant advancement in robotic policy optimization. By creating a highly accurate virtual replica of the physical robot and its operational environment, researchers can dramatically improve the fidelity of simulations. This enhanced realism allows for more effective training of robotic policies within the virtual world, addressing the notorious ‘Sim2Real’ gap – the difficulty of transferring policies learned in simulation to the complexities of the real world. Through iterative refinement within the DigitalTwin, algorithms can be thoroughly tested and optimized before deployment, minimizing risks and maximizing performance when interacting with the physical robot and its surroundings. This approach not only accelerates the development process but also fosters the creation of robust and adaptable robotic systems capable of navigating unpredictable, real-world scenarios.

Recent advancements in robotic systems have culminated in a refined architecture demonstrating up to 90% success rates across three distinct collaborative tasks. This performance is achieved through the integration of the MIND-2 dataset and the application of Implicit Q-Learning (IQL), a reinforcement learning technique allowing for efficient policy optimization. Researchers posit that this system represents a crucial stepping stone toward the development of truly versatile robots, capable of navigating and interacting effectively within complex and unpredictable real-world settings. The demonstrated capabilities suggest a future where robots can reliably assist in a diverse array of tasks, moving beyond highly structured environments and into the dynamic landscapes of human activity.

The creation of RoboMIND 2.0 reflects a pursuit of essential structure in the field of embodied intelligence. The dataset’s emphasis on multi-modal learning-integrating vision, language, and tactile sensing-is not merely an accumulation of data, but a distillation of relevant information. As Carl Friedrich Gauss observed, “If I have seen further it is by standing on the shoulders of giants.” This dataset builds upon prior work, offering a more complete sensory picture for robotic systems. The framework’s focus on sim-to-real transfer aims to minimize superfluous complexity, allowing for policies applicable across diverse environments-a demonstration of elegance through reduction.

The Road Ahead

The proliferation of datasets in robotics has, at times, resembled a frantic accumulation of parts, hoping assembly will occur spontaneously. RoboMIND 2.0 appears to acknowledge this, offering not merely more data, but a structured attempt at multimodal correlation. The dual-system framework is a welcome simplification-they called it innovation, but it felt like a return to first principles. Yet, the true test isn’t in achieving benchmarks, but in exposing the limitations of the benchmarks themselves.

Generalization remains the elusive phantom. Sim-to-real transfer, while improved, still feels less like true understanding and more like a carefully constructed illusion. The dataset’s strength lies in its scale, but the field would do well to consider diminishing returns. Perhaps the next step isn’t simply ‘more’, but ‘smarter’-algorithms that actively question the data, identifying biases and inconsistencies rather than passively absorbing them.

Tactile sensing, rightly highlighted, is often treated as an afterthought. It is a curious habit, this insistence on visual dominance in a world experienced through multiple senses. Future work might profitably focus not on replicating human dexterity, but on discovering what forms of manipulation are sufficient – recognizing that elegance lies not in mirroring complexity, but in achieving results with minimal intervention.

Original article: https://arxiv.org/pdf/2512.24653.pdf

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

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2026-01-01 15:26