Feeling Their Way to Safety: Reinforcement Learning and Robotic Touch

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Author: Denis Avetisyan

A new approach combines reinforcement learning with force and tactile sensing to enable robots to perform delicate manipulation tasks more safely and reliably.

This review details how integrating haptic perception improves sim-to-real transfer and enhances robot safety in complex object handling scenarios.

While reinforcement learning holds promise for complex robotic manipulation, ensuring stability and safe real-world deployment remains a significant challenge. This paper, ‘Reinforcement Learning for Robotic Safe Control with Force Sensing’, addresses this limitation by integrating force and tactile sensing into the learning process. Our approach demonstrates improved adaptability, safety, and successful transfer from simulation to real-world scenarios, particularly in object pushing tasks. Could this force-informed reinforcement learning paradigm unlock more robust and reliable robotic systems for a wider range of applications?

The Imprecise Reality of Robotic Manipulation

Conventional robotic manipulation frequently depends on meticulously crafted models of both the robot itself and its surrounding environment. However, this approach encounters significant challenges when applied to real-world scenarios, which are inherently imperfect and unpredictable. These models often fail to account for factors like imprecise sensor data, variations in object properties – such as unexpected slipperiness or deformation – and the dynamic nature of the environment. Consequently, even slight discrepancies between the modeled world and reality can lead to substantial errors in execution, hindering a robot’s ability to reliably grasp, move, or assemble objects. This reliance on precise representations limits the adaptability and robustness of robotic systems, restricting their deployment in unstructured or dynamic settings where unforeseen circumstances are commonplace.

Robotic systems designed with rigid expectations for their surroundings often encounter difficulties when faced with the inherent messiness of the real world. Traditional manipulation strategies, predicated on meticulously mapped environments and precisely known object properties, exhibit a marked decline in performance when presented with unexpected obstacles, variations in lighting, or objects differing from those used in training. This inflexibility stems from a reliance on pre-programmed routines and a limited capacity to dynamically adjust to unforeseen circumstances. Consequently, these robots struggle with even slight deviations from ideal conditions, hindering their ability to operate reliably in dynamic spaces and severely restricting their adaptability to novel tasks or unfamiliar objects. The inability to generalize beyond controlled settings remains a significant obstacle to the widespread deployment of robots in unstructured, real-world applications.

The seemingly simple act of pushing an object – a cornerstone of many robotic tasks – demands a surprisingly sophisticated interplay between perception and control. A robot must not only accurately perceive the object’s shape, weight, and surface properties, but also the dynamic interaction forces at play during contact. Successful manipulation isn’t about rigidly following a pre-programmed trajectory; instead, it requires continuous sensing to adapt to subtle changes in friction, unexpected collisions, or variations in the object’s initial state. Control strategies must therefore move beyond precise positioning and embrace force regulation, impedance control, and even learning-based approaches that allow the robot to refine its pushing technique over time. Ultimately, achieving robust object pushing capabilities necessitates a system that can ‘feel’ its way through an interaction, responding intelligently to the complex physics of the real world and ensuring both task completion and safe operation.

Robotic manipulation systems, despite advancements in hardware and algorithms, consistently encounter difficulties when transitioning between controlled laboratory settings and the complexities of real-world environments. A primary obstacle lies in their limited ability to generalize learned behaviors; a robot expertly assembling components under ideal conditions may falter when faced with slight variations in lighting, object position, or even the presence of unexpected obstacles. This lack of robustness isn’t simply a matter of accuracy, but also of safety; unpredictable interactions can lead to collisions, damage to the robot or its surroundings, and potentially harmful contact with humans. Consequently, significant research focuses on developing algorithms that enable robots to adapt to changing conditions, predict potential hazards, and maintain safe and reliable performance even when confronted with the inherent uncertainty of the physical world.

Adaptive Control Through Reinforcement Learning

Traditional robotic control relies on pre-programmed behaviors or human-defined controllers, which struggle with adaptability and unforeseen circumstances. Reinforcement learning (RL) provides an alternative paradigm where a robotic agent learns an optimal control policy through direct interaction with its environment. This learning process involves the agent performing actions, receiving feedback in the form of rewards or penalties, and adjusting its behavior to maximize cumulative reward. Unlike supervised learning, RL does not require labeled datasets; the robot autonomously discovers effective strategies through trial and error. This approach enables robots to adapt to dynamic environments and learn complex behaviors without explicit programming, improving robustness and autonomy in tasks such as navigation, manipulation, and locomotion.

A well-defined reward function is critical for successful Reinforcement Learning (RL) implementation, as it provides the scalar signal used to evaluate the agent’s actions and drive learning. This function maps each state-action pair to a numerical reward, indicating the immediate desirability of that action in that state. The reward function must accurately reflect the desired task objective; ambiguities or misalignments can lead to unintended behaviors or suboptimal policies. Designing an effective reward function often involves careful consideration of the task’s nuances, potential pitfalls, and the trade-offs between immediate and long-term rewards. Furthermore, reward shaping – the process of providing intermediate rewards to guide learning – can be necessary to accelerate convergence, particularly in complex environments with sparse rewards, but must be implemented cautiously to avoid introducing unintended biases.

Deep Deterministic Policy Gradient (DDPG) extends reinforcement learning capabilities by utilizing deep neural networks to approximate both the policy function, which maps states to actions, and the value function, which estimates the expected cumulative reward for a given state. This function approximation is crucial for handling high-dimensional state and action spaces common in robotics. DDPG employs an actor-critic architecture, where the actor network learns the deterministic policy $\mu(s)$ and the critic network estimates the $Q$-value $Q(s, a)$. The critic is trained using the Bellman equation and temporal difference learning, while the actor is updated using the policy gradient theorem, maximizing the $Q$-value predicted by the critic. This approach allows DDPG to learn continuous control policies directly from raw sensory input, a significant advancement over traditional methods reliant on discrete action spaces or hand-engineered features.

Hindsight Experience Replay (HER) is a reinforcement learning technique designed to improve sample efficiency, particularly when rewards are infrequent. In standard RL, episodes yielding no reward are often discarded, leading to wasted data. HER addresses this by relabeling these failed trajectories as if the agent had achieved a different, attainable goal. Specifically, after an episode, the actual achieved state is treated as the desired goal, and the trajectory is replayed with this new goal. This effectively transforms a failure into a successful experience from a different perspective, providing a denser learning signal and accelerating policy improvement in sparse-reward environments where achieving any reward is initially unlikely. The technique is applicable to goal-conditioned reinforcement learning where the agent receives a reward based on reaching a specified goal state.

Embodied Perception: The Role of Force and Tactile Sensing

Force-Based Reinforcement Learning (FBLRL) extends traditional Reinforcement Learning (RL) by directly incorporating data from force and tactile sensors into the reward function and state space. This integration allows robotic agents to learn policies that explicitly account for physical interactions with the environment, increasing robustness to disturbances and improving safety during operation. Instead of solely relying on position or velocity feedback, FBLRL enables robots to react to unexpected contacts, modulate applied forces, and prevent collisions. The learned policies are therefore more resilient to variations in object pose, surface properties, and external forces, leading to more reliable performance in dynamic and uncertain environments. This approach often involves defining reward terms that penalize excessive forces or deviations from desired contact states, effectively shaping the agent’s behavior towards safer and more controlled interactions.

The integration of force and tactile sensing allows robotic systems to dynamically adjust to unforeseen physical interactions with the environment. When unexpected contact occurs, these sensors provide immediate feedback regarding contact location and magnitude, enabling the robot to modify its actions in real-time. This adaptive capability extends beyond simple avoidance; robots can regulate applied forces to maintain stable grasps, perform compliant assembly, or safely interact with delicate objects. By responding to contact forces, the robot can maintain task performance despite external disturbances or inaccuracies in its environment model, improving operational robustness and reducing the risk of damage to itself or its surroundings.

Tactile sensing provides robots with detailed information about physical contact, which is essential for successful grasping and manipulation. These sensors detect forces and textures, allowing the robot to determine object shape, orientation, and stability during interaction. Data from tactile sensors enables closed-loop control of grip force, preventing slippage or damage to fragile objects. Furthermore, tactile feedback allows robots to adapt to variations in object properties and environmental conditions, improving the reliability and dexterity of manipulation tasks. High-resolution tactile sensors can detect subtle features, such as edges and contours, aiding in precise object localization and pose estimation independent of visual input.

Accurate pose estimation is a foundational requirement for force and tactile sensing integration in robotic systems. Visual methods employing ARUCO markers are frequently utilized to achieve this, providing a readily detectable and computationally efficient means of determining the robot’s and objects’ position and orientation within the workspace. These markers, characterized by their unique binary encoding, facilitate robust tracking even under varying lighting conditions and partial occlusions. The estimated pose data is then used to interpret force and tactile readings, allowing the robot to accurately determine contact locations and forces applied to objects, and enabling precise manipulation and control. Marker-based systems typically involve camera calibration to establish the relationship between pixel coordinates and world coordinates, and pose is calculated using algorithms like Perspective-n-Point (PnP).

Bridging the Reality Gap: Sim-to-Real Transfer

The ambition of Sim-to-Real Transfer lies in enabling robotic systems to learn complex behaviors within the safety and efficiency of simulated environments, and then directly apply those learned policies to physical robots operating in the real world. This approach bypasses the extensive and often costly process of training robots through trial and error in a physical setting, instead leveraging the power of computational simulation. By training a policy – essentially a set of rules guiding the robot’s actions – entirely in simulation, researchers aim to create robotic systems capable of swiftly adapting to new tasks and environments without requiring lengthy real-world calibration. The core challenge, however, centers around the inevitable discrepancies between the simulated and real worlds, a phenomenon known as the ‘domain gap’, which often leads to diminished performance when transferring the learned policy to a physical robot.

A fundamental challenge in deploying robotic systems trained in simulated environments lies in the disparity between the virtual world and the complexities of reality – a phenomenon known as the domain gap. This gap arises from discrepancies in dynamics, sensor noise, friction, and unmodeled physical interactions, causing policies learned in simulation to perform poorly when transferred to a real robot. Even highly realistic simulations cannot perfectly replicate the nuanced and often unpredictable nature of the physical world; consequently, a robot might confidently execute a task in simulation, only to fail or behave erratically in a real-world scenario. Bridging this gap is therefore paramount for the successful implementation of simulation-based robotic learning, requiring techniques that enhance the robustness and adaptability of learned policies to overcome these inherent differences.

Force-based reinforcement learning offers a compelling solution to the challenges of transferring robotic skills from simulated environments to the complexities of the real world. By explicitly incorporating force and tactile feedback into the learning process, robots develop control strategies that are inherently more robust to discrepancies between the simulation and reality – often referred to as the ‘domain gap’. Instead of relying solely on visual data or positional information, these algorithms allow robots to feel their way through tasks, adapting to unexpected contact forces and surface properties. This approach doesn’t just improve performance in simulation; it demonstrably enhances success rates when the learned policies are deployed on physical robots, enabling them to reliably execute tasks even in the presence of noise, imperfect models, and unpredictable external disturbances. The result is a significant leap toward creating robots capable of seamless and adaptable operation in real-world scenarios.

Successful robotic deployment hinges not only on skillful task execution but also on ensuring safe physical interactions with the environment and humans. Recent advances demonstrate that incorporating force and tactile sensing data directly into reinforcement learning algorithms provides a critical pathway to robust and secure control. By explicitly modeling safety constraints – derived from these sensor inputs – the resulting policies exhibit a markedly improved capacity to navigate unpredictable real-world scenarios. Notably, this approach circumvents the need for computationally expensive and often imperfect domain adaptation techniques, achieving higher success rates in physical deployments directly from simulation without requiring intermediate adjustments to bridge the gap between virtual and real environments. The method’s efficacy lies in its proactive approach to collision avoidance and force regulation, resulting in more reliable and secure robotic operation.

The pursuit of robust robotic control, as detailed in this work, echoes a fundamental tenet of computer science. The integration of force sensing, allowing the robot to ‘feel’ its environment, is not merely a practical enhancement, but a step towards provable correctness in manipulation tasks. This aligns with Barbara Liskov’s assertion: “It’s one thing to make a program work; it’s another to make it correct.” The paper’s emphasis on sim-to-real transfer, mitigating the risks inherent in real-world deployment, highlights the importance of building systems that behave predictably and reliably, moving beyond solutions that merely ‘work’ on limited test cases to those grounded in verifiable principles.

The Road Ahead

The integration of force sensing into reinforcement learning frameworks, as demonstrated, represents a necessary, though hardly sufficient, step toward robust robotic manipulation. The persistent challenge remains not merely to react to contact, but to predict it with mathematical certainty. Current approaches rely heavily on empirical observation – training regimes that, while yielding demonstrable success in controlled environments, offer little guarantee against unforeseen circumstances. The elegance of a truly generalized solution necessitates a formalism capable of deducing safe trajectories, not merely discovering them through trial and error.

Future work must address the inherent limitations of sim-to-real transfer. While domain randomization offers a palliative, it is, at its core, a brute-force approach. A more principled solution demands a deeper understanding of the underlying physical principles governing contact and deformation – a move toward models grounded in continuum mechanics, rather than pixel-based approximations. Every parameter tuned in simulation introduces a potential source of error; the goal should be to minimize such dependencies, striving for solutions that are invariant to environmental variations.

Ultimately, the pursuit of ‘safe’ control should not be conflated with merely ‘reactive’ control. The aspiration is not to build robots that avoid collisions, but rather to build robots capable of reasoning about their interactions with the physical world – machines whose actions are dictated by logical necessity, not statistical likelihood. The elimination of redundancy, both in sensing and control, remains paramount.

Original article: https://arxiv.org/pdf/2512.02022.pdf

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

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2025-12-03 11:08