Robots Get a Brain Boost: The Rise of Intelligent Manipulation

Author: Denis Avetisyan

This review explores how recent advances in artificial intelligence are transforming robotic manipulation, enabling robots to perform increasingly complex tasks.

This work presents a comprehensive survey of embodied manipulation, beginning with an overview of high-level planning architectures and focusing specifically on learning-based strategies for low-level control implementation.

A comprehensive survey of planning and learning techniques for embodied robot manipulation in the era of foundation models, addressing challenges and future directions.

Despite decades of robotics research, achieving robust and generalizable robot manipulation remains a central challenge. This survey, ‘Embodied Robot Manipulation in the Era of Foundation Models: Planning and Learning Perspectives’, systematically examines recent advances in this field, framing them within a unified perspective of high-level planning and low-level learning-based control. We demonstrate how modern approaches leverage language, vision, and multimodal data to bridge the gap between abstract task specifications and physical execution. Looking ahead, can these emerging foundation models truly unlock the potential for robots to interact with and adapt to complex, real-world environments?

The Challenge of Embodied Intelligence

Conventional robotics often falters when confronted with the unpredictable nature of real-world settings. These systems, typically designed for highly structured tasks, exhibit limited capacity to adapt to novel situations or generalize learned behaviors beyond their initial training parameters. This inflexibility stems from a reliance on precise programming and pre-defined models of the environment, which struggle to account for the inherent variability and complexity found in unstructured spaces. Consequently, even seemingly minor deviations – such as changes in lighting, unexpected obstacles, or variations in object appearance – can disrupt performance and necessitate manual intervention. The difficulty lies not in the robot’s ability to execute a known task, but in its capacity to understand and respond effectively to the infinite possibilities presented by a dynamic, real-world environment.

Many contemporary robotic systems are constrained by a dependence on meticulously crafted, hand-engineered solutions for even moderately complex tasks. Alternatively, achieving robust performance often necessitates training on extraordinarily large datasets – a process that is both computationally expensive and limits adaptability to novel situations. This reliance on either extensive manual design or massive data requirements presents a significant bottleneck for deployment in real-world environments, which are inherently dynamic and unpredictable. The need for constant refinement through either painstaking manual adjustments or continual data collection hinders the ability of robots to operate effectively and autonomously in constantly changing scenarios, restricting their utility beyond highly controlled or static applications.

A persistent challenge in robotics lies in the discrepancy between simulated environments and the unpredictable nature of the physical world – a phenomenon known as the ‘Sim-to-Real Gap’. Policies meticulously crafted and refined within the controlled parameters of a simulation often falter when deployed onto actual robotic hardware. This failure stems from inaccuracies in the simulation itself – simplified physics, imperfect sensor models, and the inability to fully replicate the complexities of real-world textures, lighting, and unforeseen disturbances. Consequently, a robot that navigates flawlessly in a virtual space may stumble, misjudge distances, or fail to recognize objects when operating in a genuine environment. Bridging this gap requires innovative techniques such as domain randomization – intentionally varying simulation parameters to force the robot to learn more robust policies – or employing techniques that allow robots to adapt and refine their behavior through real-world experience, effectively ‘learning’ the differences between simulation and reality.

Low-level robotic manipulation can be achieved through reinforcement learning via trial-and-error with rewards, imitation learning by mapping expert demonstrations, or auxiliary-task learning which uses self-supervision-including world modeling and goal abstraction-to shape learned representations.

Data-Driven Foundations for Robot Learning

Data-driven robot control leverages large datasets comprising recorded robot states, actions, and corresponding environmental observations to train control policies. These datasets can be generated through human demonstration, robotic experimentation, or simulation. The core principle involves utilizing machine learning algorithms to identify patterns and relationships within the data, enabling the robot to generalize learned behaviors to novel situations. This approach contrasts with traditional methods that rely on explicitly programmed rules and requires minimal hand-engineering of control logic, offering scalability and adaptability to complex tasks and dynamic environments. The size and quality of the dataset directly influence the performance and robustness of the learned control policy.

Imitation Learning (IL) and Reinforcement Learning (RL) represent core methodologies within data-driven robot learning. IL enables robots to learn a policy by observing and replicating actions demonstrated by an expert, typically requiring a dataset of state-action pairs. Conversely, RL allows a robot to learn through trial and error, receiving reward signals for successful actions and penalties for failures; this process optimizes a policy based on maximizing cumulative reward. Both techniques utilize machine learning algorithms – supervised learning is common in IL, while RL employs techniques like Q-learning or policy gradients – to map observations to actions, differing primarily in how the learning signal is acquired and utilized to refine the robot’s behavior.

Data-driven robot learning methods, specifically Imitation Learning and Reinforcement Learning, diminish the need for explicit, hand-coded control programs by enabling skill acquisition through data. Robots utilizing these techniques can generalize learned behaviors to novel situations within their operating environment. This is achieved by training on datasets comprising either expert demonstrations – providing examples of desired actions – or through autonomous exploration and reward-based learning. Consequently, robots can develop competencies in complex tasks and adapt to changing conditions without requiring developers to anticipate and program for every possible scenario, significantly reducing development time and increasing operational flexibility.

Latent learning utilizes pretrained robotic encoders to generate latent representations that guide policy actions, either through discretized or continuous latent spaces, enabling a dual-system approach where a higher-level system directs a lower-level action generator.

Planning and Control: Bridging the Conceptual Gap

High-level planners are a crucial component of robot manipulation systems, providing the necessary framework for translating desired tasks into executable actions. These planners operate by reasoning about the robot’s goals, the constraints imposed by the environment and robot capabilities, and the affordances – or potential actions – offered by objects within that environment. This reasoning process enables the generation of task plans that define a sequence of steps to achieve a specified objective, considering factors such as object locations, potential collisions, and required tool usage. Effective high-level planning is therefore fundamental for enabling robots to perform complex tasks in unstructured environments, moving beyond pre-programmed sequences to achieve adaptable and goal-directed behavior.

Recent developments in robot task planning leverage Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) to translate high-level instructions into executable plans. LLM-based approaches process natural language prompts, generating sequential task lists based on the model’s understanding of language and world knowledge. MLLM-based methods extend this capability by incorporating visual inputs, such as images or videos, allowing robots to perceive their environment and plan actions accordingly. These models utilize techniques like prompting and few-shot learning to adapt to new tasks without extensive retraining, demonstrating improved generalization and flexibility in complex manipulation scenarios. Current research focuses on enhancing the robustness and reliability of these plans, addressing challenges related to ambiguous instructions, environmental uncertainties, and the generation of physically feasible trajectories.

This survey comprehensively reviews recent progress in robotic manipulation, categorizing methodologies into high-level planning techniques and low-level learning approaches. The analysis focuses on developments enabling robots to perform increasingly complex tasks, with particular attention to the integration of these two areas. Identified challenges include improving generalization to novel scenarios, enhancing robustness to environmental disturbances, and addressing the data efficiency of learning algorithms. Future research directions emphasize the development of robotic foundation models – broadly capable systems that can adapt to a wide range of manipulation tasks with minimal task-specific training – and the associated infrastructure for data collection, model training, and evaluation.

High-level planners leverage six core components-LLM-based task planning, MLLM-based task planning, code generation, motion planning, affordance learning, and 3D scene representations-as illustrated by recent advancements [song2023llm,mu2023embodiedgpt,liang2023code,huang2023voxposer,jiang2022ditto,shen2023distilled].

Enhancing Policy Learning and Adaptability

Robust robotic performance hinges on the capacity of low-level controllers to distill meaningful insights from complex sensory input. Effective controllers don’t simply react to raw data; instead, they employ techniques like latent learning to discover underlying patterns and representations within that data, effectively compressing it into a more manageable form. This process, coupled with input modeling – which focuses on understanding the statistical properties and potential noise within the sensory signals – allows the robot to filter out irrelevant information and focus on the most critical aspects of its environment. The result is a system capable of generating stable and reliable actions, even in the face of uncertainty or disturbance, ultimately forming a crucial foundation for higher-level planning and decision-making.

The development of sophisticated robotic control hinges on advanced policy learning algorithms that translate sensory inputs into effective actions, and several distinct approaches are currently being explored. Multi-Layer Perceptron (MLP)-based policies offer a foundational, yet powerful, method for this mapping, while Transformer-based policies, inspired by natural language processing, excel at capturing long-range dependencies in complex sequential data. More recently, Diffusion Policies and Flow Matching Policies have emerged as innovative techniques, learning to generate actions through probabilistic modeling-essentially reversing a diffusion process to determine the optimal response to a given situation. Each of these algorithms presents unique strengths, influencing factors such as learning speed, adaptability to novel environments, and the ability to generalize across a range of tasks, collectively pushing the boundaries of robotic autonomy and intelligent behavior.

The capacity for robotic agents to navigate complex environments hinges not simply on perceiving the world, but on understanding how to interact with it. Combining advanced policy learning algorithms with the principles of Affordance Learning allows robots to move beyond recognizing objects to discerning the action possibilities they offer. This means a robot doesn’t just identify a chair as ‘chair,’ but understands it ‘affords’ sitting, standing upon, or even pushing. By learning these affordances – the relationships between an object’s properties and an agent’s capabilities – robots can generate more flexible and effective behaviors, adapting to novel situations and leveraging environmental features to achieve goals. This approach moves robotic interaction beyond pre-programmed routines, fostering a more nuanced and adaptable form of intelligence in dynamic, real-world settings.

The Future of Adaptive Robotics

Robots operating in dynamic environments necessitate a shift from traditional, static learning approaches to continual learning, a paradigm focused on retaining previously acquired knowledge while integrating new information. This is critical because robots, unlike humans, often suffer from ‘catastrophic forgetting’ – the tendency to abruptly lose performance on older tasks when learning new ones. Continual learning combats this by employing strategies that preserve relevant past experiences, allowing the robot to build upon its skillset over time. Through techniques like experience replay, regularization, and dynamic network expansion, robots can not only avoid forgetting but also leverage past knowledge to accelerate learning on subsequent tasks – a process known as positive transfer. Ultimately, this ability to accumulate and generalize knowledge is fundamental to creating robots capable of sustained, autonomous operation and adaptation in the real world.

The trajectory of robotics hinges on a synergistic approach, integrating data-driven learning with sophisticated planning and resilient policy algorithms. Data-driven methods allow robots to learn directly from experience, refining their actions through vast datasets and minimizing the need for explicit programming. However, raw data alone is insufficient; advanced planning architectures provide the foresight to anticipate future states and formulate effective strategies. Crucially, robust policy learning algorithms ensure that these strategies remain stable and adaptable, even in unpredictable environments. This convergence – the ability to learn from data, plan strategically, and maintain consistent performance – represents a fundamental shift, moving robotics beyond pre-programmed routines towards genuine intelligence and unlocking the potential for robots to tackle increasingly complex and dynamic challenges.

The development of increasingly adaptable and resilient robots promises a transformative impact across numerous real-world applications. These future machines will move beyond pre-programmed tasks, exhibiting the capacity to learn from experience and adjust to unforeseen circumstances – a crucial ability for navigating the inherent unpredictability of environments like disaster zones or even domestic homes. This enhanced capability extends beyond simple reaction; robots will proactively solve complex problems, integrating data from multiple sources and employing advanced planning to achieve goals even when facing novel challenges. Consequently, sectors ranging from manufacturing and logistics to healthcare and space exploration stand to benefit from robotic systems capable of independent operation, improved efficiency, and a heightened capacity for innovation.

The survey highlights a critical juncture in robotic manipulation – the convergence of high-level planning with low-level, learning-based control. This echoes Vinton Cerf’s observation: “The Internet treats everyone the same.” While seemingly disparate, both concepts emphasize a foundational principle: effective systems require seamless integration. Just as the internet prioritizes uniform access, robust robotic foundation models demand that planning and learning aren’t siloed, but operate in concert. The challenge, as the survey details, lies in bridging the abstraction gap – ensuring that high-level goals translate effectively into low-level actions, a process mirroring the internet’s need for standardized protocols to facilitate communication between diverse systems.

What’s Next?

The pursuit of robotic foundation models, as outlined in this survey, reveals a curious tendency. The field attempts to graft high-level reasoning onto systems still fundamentally reliant on brittle, low-level reflexes. Success hinges not simply on scaling data or model parameters, but on a deeper understanding of the inherent limitations of disembodied intelligence. The current focus on vision-language models, while promising, often treats manipulation as a purely visual problem, overlooking the crucial role of tactile sensing and physical interaction. True robustness will require a shift towards models that understand materials, forces, and the consequences of action – not merely predict them.

The challenge, predictably, isn’t solely technical. The quest for ‘general-purpose’ robots implies a universality that nature rarely exhibits. Specialization, adaptation, and the acceptance of imperfection are often more effective strategies than striving for a single, all-encompassing solution. Future research must grapple with the question of what constitutes ‘general’ – and whether it’s even a desirable goal, or simply a reflection of human biases. Sim-to-real transfer, a persistent bottleneck, will not be solved by increasingly realistic simulations, but by fundamentally rethinking the relationship between virtual and physical experience.

Ultimately, the architecture of these systems will determine their capacity for true intelligence. Good architecture is invisible until it breaks, and only then is the true cost of decisions visible.

Original article: https://arxiv.org/pdf/2512.22983.pdf

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