Robots That Touch: Navigating Safe Learning for Complex Manipulation

Author: Denis Avetisyan

This review explores the evolving landscape of safe learning techniques enabling robots to perform intricate, contact-rich tasks without causing harm or damage.

Research increasingly focuses on the convergence of safety, contact-rich robotics, and learning-based methodologies, with studies exploring advancements in areas such as manipulation tasks, secure control strategies, and intelligent systems that adapt through experience.

A comprehensive survey of methods, from classical learning-based approaches to the potential and challenges of safe foundation models like vision-language-action models.

Despite advances in robotic manipulation, ensuring safe and reliable performance in contact-rich tasks remains a significant challenge due to inherent uncertainties and potential for damage. This survey, ‘Safe Learning for Contact-Rich Robot Tasks: A Survey from Classical Learning-Based Methods to Safe Foundation Models’, provides a comprehensive overview of learning-based methods designed to address these safety concerns, categorizing approaches for both safe exploration and execution. It highlights how techniques like constrained reinforcement learning and control barrier functions are evolving alongside emerging robotic foundation models-particularly vision-language models-to balance safety and efficiency. As these models offer new opportunities for specifying constraints and grounding safety signals, a critical question arises: how can we effectively leverage their power while mitigating amplified risks in complex, real-world environments?

Navigating Complexity: The Challenge of Adaptive Robotics

Conventional robotic systems, designed for structured and predictable settings, frequently encounter difficulties when deployed in the messy reality of human environments. These robots typically rely on precise pre-programming and detailed environmental maps, which become liabilities when faced with unforeseen obstacles, dynamic changes, or the unpredictable movements of people. A robot programmed to perform a specific assembly task, for instance, may falter if a tool is slightly misplaced, or be unable to react safely to a human entering its workspace. This limitation stems from a reliance on static models and a lack of inherent adaptability, hindering their effective integration into complex, real-world scenarios where flexibility and robust perception are crucial for successful operation and, most importantly, for ensuring human safety.

Conventional robotic systems, reliant on meticulously crafted pre-programmed sequences, often falter when confronted with the inherent variability of real-world scenarios. To overcome these limitations, researchers are increasingly focused on intelligent learning techniques – algorithms that enable robots to acquire skills and adapt their behavior through experience, much like humans do. This shift involves employing methods like reinforcement learning, where robots learn through trial and error, and imitation learning, where they acquire skills by observing demonstrations. These approaches allow for the development of robots capable of generalizing beyond their initial training, navigating unforeseen obstacles, and responding dynamically to changing circumstances, ultimately paving the way for more versatile and reliable robotic partners in complex environments.

In scenarios demanding physical interaction – such as collaborative robotics or surgical assistance – the imperative for unwavering safety cannot be overstated. Even seemingly insignificant errors in a robot’s movements or force application can precipitate damage to equipment or, critically, inflict injury upon humans. Unlike purely automated systems operating in controlled environments, robots in contact-rich tasks constantly negotiate unpredictable external forces and subtle variations in their surroundings. Consequently, developers are prioritizing the implementation of redundant safety mechanisms, advanced sensor fusion, and real-time error detection algorithms. These systems strive to not only prevent collisions but also to anticipate potential hazards and modulate robotic behavior accordingly, ensuring reliable and secure operation even in the face of unforeseen circumstances. The pursuit of absolute safety remains a central challenge, driving innovation in areas like compliant actuators, force-torque sensing, and the development of formal verification techniques to guarantee predictable and safe robot responses.

A data pyramid approach leverages increasingly abstract data sources-from real-world execution logs and high-fidelity simulations to web-scale datasets-to enable safe policy learning for complex, contact-rich manipulation tasks.

Establishing a Foundation: Safe Learning for Intelligent Systems

Safe Learning methodologies are crucial for robotic systems operating in complex and potentially hazardous environments, as they prioritize safety during both the learning and deployment phases. Traditional reinforcement learning algorithms can exhibit unpredictable behavior during exploration, leading to collisions or violations of operational constraints. Safe Learning techniques mitigate these risks by incorporating mechanisms to guarantee that the robot’s actions remain within predefined safety boundaries. This is achieved through methods that constrain the learning process, ensuring that explored policies do not lead to unsafe states, and by providing guarantees on the robot’s behavior during execution, even in the face of disturbances or uncertainties. The fundamental requirement is to enable continuous learning and adaptation without compromising the safety of the robot itself, nearby humans, or the environment.

Constraint Satisfaction and Control Barrier Function (CBF) control are key components of safe learning methodologies for robotics. Constraint Satisfaction techniques define permissible states and actions, ensuring robot behavior adheres to specified limits-such as joint angles or workspace boundaries. CBF control builds upon this by formulating safety constraints as a Lyapunov-like function, guaranteeing that the robot’s dynamics remain within these safe sets during operation. Specifically, CBFs are incorporated into the control law to prevent the system from violating safety constraints, even when faced with disturbances or model uncertainties. The resulting control actions effectively steer the robot towards desired goals while simultaneously maintaining safety, providing formal guarantees about the robot’s behavior within predefined boundaries.

This survey details current methodologies in safe reinforcement learning specifically applied to robotic tasks involving frequent physical contact with environments and objects. The review encompasses techniques designed to prevent unsafe actions during both the learning process and deployment. A primary focus is the analysis of how emerging foundation models – including Vision-Language Models (VLMs) and Vision-Action Models (VLAs) – impact the field. The survey identifies challenges in adapting these large-scale models for safe robotic control, such as generalization to novel situations and ensuring constraint satisfaction, while also outlining opportunities for leveraging their capabilities to improve safety and robustness in contact-rich manipulation and locomotion tasks.

Contact-rich safe learning metrics balance safety, task success, and efficiency, often with trade-offs, and can be measured using indicators like force, velocity, and energy.

Perception and Control: Enhancing Robust Physical Interaction

MultiModal Sensing integrates data from multiple sensor types – including force/torque sensors, tactile sensors, and vision systems – to provide a more comprehensive representation of a robot’s environment and its interactions. Contact Force Estimation, a key component of this approach, specifically calculates the forces and torques exerted during physical contact with objects. These estimations are not limited to end-effector forces; they can also include forces distributed across a robot’s body. Combining these diverse data streams allows robots to determine object properties like shape, weight, and material composition, as well as to accurately perceive contact states such as slipping, jamming, or stable grasp. This enriched perception is crucial for tasks requiring delicate manipulation, assembly, or adaptive interaction with uncertain environments, moving beyond reliance on position-only feedback.

Impedance control and passivity control are techniques used in robotics to manage the forces and motions exerted during physical interaction with environments or objects. Impedance control defines a desired relationship between force and position, allowing the robot to comply with external forces rather than rigidly resisting them; this is typically expressed as $F = K(x_d – x) + B(\dot{x}_d – \dot{x})$, where $F$ is the force, $x$ is the actual position, $x_d$ is the desired position, and $K$ and $B$ represent stiffness and damping, respectively. Passivity control, a more conservative approach, ensures the robot dissipates energy during interaction, guaranteeing stability by preventing the robot from adding energy to the system. Both methods minimize the risk of collisions and allow for safer, more adaptable interactions, particularly in applications involving human-robot collaboration or uncertain environments.

Vision-based control utilizes data from cameras to provide real-time feedback for robot manipulation and locomotion, particularly during tasks involving frequent contact with the environment. This approach typically involves processing visual information to determine object pose, distance, and velocity, which are then used as inputs to the robot’s control system. Algorithms employed range from simple image-based servoing to more complex techniques like visual servo control ($VSC$) and model predictive control ($MPC$) incorporating visual data. The adaptability of vision-based control stems from its ability to handle uncertainties in object position and orientation, and to adjust robot actions dynamically based on observed changes, resulting in improved precision and robustness in contact-rich scenarios like assembly, grasping, and surgical procedures.

ChatGPT 5.1 generated illustrative examples of contact-rich robotic tasks, including surface grinding, peg-in-hole assembly, massage, and non-prehensile manipulation.

Bridging the Reality Gap: From Simulation to Robust Deployment

Reinforcement learning empowers robots to autonomously develop sophisticated behaviors through trial and error, much like a human learning a new skill. However, allowing a robot to freely explore and learn without boundaries presents significant safety challenges in real-world applications. Consequently, the field of Constrained Reinforcement Learning has emerged as a critical component, focusing on methods that guarantee safe exploration and operation. These techniques incorporate constraints – defined by factors like physical limitations, environmental boundaries, or desired operational parameters – into the learning process. By penalizing or preventing actions that violate these constraints, Constrained RL ensures that a robot learns effectively while remaining within acceptable safety margins, paving the way for deployment in sensitive environments and complex tasks where unpredictable behavior could have serious consequences. This proactive approach to safety is essential for building trust and realizing the full potential of autonomous robotic systems.

Roboticists increasingly rely on Sim-to-Real transfer to overcome the limitations of training robots directly in the physical world. This approach allows for extensive learning within detailed simulations, where data is abundant and experimentation is inexpensive and safe. The knowledge – often in the form of learned policies or models – is then transferred to the real robot, significantly accelerating the learning process and reducing the need for prolonged, potentially damaging, real-world trials. Effective Sim-to-Real techniques address the inherent discrepancies between the simulated and physical environments – such as differences in friction, sensor noise, or dynamics – through methods like domain randomization, where the simulation parameters are varied to force the robot to learn robust strategies. This ultimately enables robots to adapt more quickly and reliably to the complexities of real-world tasks, lowering development costs and expanding the range of achievable applications.

A comprehensive review of current literature was undertaken to map the expansive design space for safe reinforcement learning in robotics. This synthesis identified key algorithms, simulation environments, and transfer techniques, revealing a fragmented yet rapidly evolving field. The analysis categorized approaches based on their handling of constraints – from reward shaping and penalty functions to shielding mechanisms and formal verification – offering a structured overview for researchers and practitioners. By consolidating findings from diverse sources, the review highlights promising avenues for future work, particularly in addressing the challenges of real-world deployment and ensuring robust, reliable robot behavior while minimizing risk during the learning process.

Looking Ahead: The Future of Safe and Intelligent Robotics

Autonomous robotic exploration demands more than simply navigating an environment; it requires a proactive approach to safety, achieved through the implementation of ‘SafeExploration’ algorithms. These algorithms allow robots to venture into unfamiliar territories while simultaneously assessing and mitigating potential risks. Unlike traditional path-planning which prioritizes reaching a destination, SafeExploration centers on maintaining a ‘safety radius’ around the robot, dynamically adjusting trajectories to avoid collisions or precarious situations. This is often accomplished by incorporating uncertainty into the robot’s understanding of its surroundings – acknowledging that sensors aren’t perfect and predictions about the environment aren’t absolute. Advanced techniques leverage probabilistic models and reinforcement learning to enable robots to learn safe exploration strategies through simulation and real-world experience, effectively balancing the desire to discover new areas with the critical need to operate without causing harm to themselves or their surroundings. Ultimately, robust SafeExploration is not merely a technical hurdle, but a foundational requirement for widespread robotic deployment in complex and unpredictable environments.

Recent advancements in robotics are increasingly focused on bridging the communication gap between humans and machines through Vision-Language Models (VLMs) and Vision-and-Language-Action models (VLAs). These models empower robots to interpret complex natural language instructions – going beyond simple commands to understand nuanced requests involving spatial relationships, object attributes, and even abstract concepts. Unlike traditional programming which requires precise code, VLMs and VLAs allow for intuitive task assignment; a user might simply state “bring me the red book from the top shelf,” and the robot, leveraging its visual perception and language understanding, can autonomously execute the request. This capability dramatically expands the range of tasks robots can perform in unstructured environments, paving the way for more versatile assistance in homes, workplaces, and beyond, and representing a significant leap towards truly collaborative human-robot interaction.

Progress in safe and intelligent robotics isn’t simply a matter of building more powerful machines; several fundamental challenges demand focused research. Data scarcity consistently hinders the development of robust algorithms, as training robots requires vast amounts of real-world interaction data which is often expensive or dangerous to collect. Equally pressing is the need for rigorous certification processes; establishing verifiable safety standards for autonomous robots operating in complex environments is crucial for public trust and widespread adoption. Furthermore, accurate modeling of both the robot’s capabilities and the surrounding environment remains a significant hurdle, especially when dealing with unpredictable dynamics and unforeseen obstacles. Finally, effectively enforcing constraints – ensuring the robot operates within defined safety parameters and legal boundaries – presents a complex technical and ethical challenge, requiring innovative solutions in control systems and verification methods.

The survey meticulously details the complexities inherent in enabling robots to perform contact-rich tasks safely, a challenge deeply rooted in the interconnectedness of system components. This holistic view echoes a fundamental tenet of robust design. As Linus Torvalds aptly stated, “Talk is cheap. Show me the code.” This sentiment aligns perfectly with the article’s emphasis on demonstrable safety guarantees and formal verification methods. The progression from classical learning-based approaches to the potential of VLMs and VLAs isn’t merely a technological shift; it’s a restructuring of the problem itself, demanding a cohesive understanding of how perception, language, and action interact to ensure predictable and safe robot behavior. Each advancement, each simplification in the learning process, carries inherent risks that must be carefully evaluated and mitigated, much like the careful coding Torvalds champions.

Where Do We Go From Here?

The pursuit of safe learning for contact-rich manipulation reveals a persistent truth: robustness isn’t simply a matter of algorithmic complexity. The field has, perhaps predictably, focused on increasingly sophisticated models – vision-language constructs being the current fascination – yet the fundamental challenge remains the brittle nature of systems built upon incomplete understandings of physical interaction. Scalable solutions won’t emerge from larger parameter counts, but from clearer, more concise representations of contact dynamics and task constraints. The ecosystem of robot, environment, and learning algorithm demands holistic consideration; optimizing one component at the expense of the others invites predictable failure.

Current approaches, while demonstrating incremental progress, largely treat safety as a post-hoc constraint – a bandage applied to an inherently unstable system. A more elegant path lies in designing for safety from the outset, embedding physical principles and verifiable properties directly into the learning framework. This necessitates a shift from purely data-driven methods towards hybrid approaches that leverage both learning and formal verification – acknowledging that empirical success, however compelling, offers no guarantee of true reliability.

The looming question isn’t whether vision-language models can describe manipulation, but whether they can contribute to a deeper, more principled understanding of it. The true metric of progress won’t be benchmark scores, but the ability to build systems that predictably, and safely, navigate the inherent uncertainties of the physical world. The elegance of a solution, after all, is measured not by its complexity, but by its capacity to reveal the underlying simplicity of the problem.

Original article: https://arxiv.org/pdf/2512.11908.pdf

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