Handing Off Control: Safe Robot Collaboration with Reinforcement Learning

Author: Denis Avetisyan

Researchers have developed a new framework that allows robots to reliably and safely accept objects from a human hand using reinforcement learning and a novel safety system.

The system learns adaptive motion profiles for close human-robot interaction through reinforcement learning, utilizing a rigid-body constraint for object manipulation and incorporating normal contact force-$FNF\_N$-as feedback within a safety-focused reward function.

ContactRL combines reinforcement learning with a kinetic energy-based safety shield to enable robust and safe contact-rich human-robot collaboration.

Achieving truly collaborative robot behavior requires moving beyond simple collision avoidance to encompass safe, intentional physical contact-a challenge often unmet by existing motion planning approaches. This paper introduces ContactRL: Safe Reinforcement Learning based Motion Planning for Contact based Human Robot Collaboration, a novel framework that integrates force feedback directly into a reinforcement learning reward function to optimize for both task efficiency and contact safety. Through simulation and real-world experiments with a UR3e robot performing object handovers, we demonstrate that ContactRL achieves a low safety violation rate alongside high task success, further augmented by a kinetic energy-based safety shield. Could this approach unlock more robust and intuitive physical human-robot interaction in complex, contact-rich environments?

The Inevitable Dance: Designing for Human-Robot Coexistence

The expanding presence of robots in human-centric environments – from manufacturing floors and healthcare facilities to domestic spaces – is driving a critical need for enhanced safety protocols. This isn’t merely about preventing collisions; it’s about designing systems that can operate alongside people without posing a risk to their well-being. Historically, robotic systems were isolated, often caged, to avoid interaction; however, the demand for collaborative robots – those intended to work directly with humans – necessitates a fundamental shift in design philosophy. Current deployments see robots assisting in surgery, providing companionship, and even delivering goods, all of which require a nuanced understanding of human movement, force exertion, and potential vulnerabilities. Consequently, researchers are actively developing advanced sensing technologies, predictive algorithms, and compliant actuators to ensure that these increasingly ubiquitous machines can navigate and interact with people safely and effectively, minimizing the potential for harm and fostering trust in human-robot collaboration.

Conventional robot control systems, designed primarily for predictable industrial environments, frequently falter when interacting with humans due to the inherent uncertainty of human behavior and physiology. These systems typically rely on reactive safety measures – stopping or reducing force upon detecting collision – which can be slow and may not prevent injury, particularly with vulnerable populations. The challenge lies in the difficulty of accurately modeling and predicting the complex, often unpredictable, contact forces generated during human-robot interaction. Existing impedance control methods, while improving responsiveness, often struggle to balance responsiveness with stability, leading to jerky movements or insufficient force accommodation. Consequently, a robot operating under these constraints may unintentionally exert harmful pressure, or fail to provide necessary assistance, highlighting the limitations of purely reactive approaches to ensuring human well-being in collaborative scenarios.

The development of genuinely collaborative robots hinges on moving beyond reactive safety measures – those that simply halt operation upon detecting a collision – and embracing proactive strategies. Current systems often struggle with the nuances of human movement and unpredictable contact, necessitating control algorithms capable of anticipating and adapting to human intent. This requires robots to not only sense their environment with greater fidelity, but also to model human behavior and predict potential interactions. Researchers are exploring techniques like reinforcement learning and impedance control to allow robots to dynamically adjust their movements and forces, ensuring a safe yet fluid interaction. Ultimately, the goal is to create robots that can seamlessly work alongside humans, sharing workspaces and tasks without requiring constant monitoring or cumbersome safety protocols, fostering a truly collaborative and efficient partnership.

This simulation depicts a UR3e robot interacting with an object using a simplified hand model, with the right panel detailing the contact forces experienced during manipulation.

ContactRL: Sculpting Safety Through Learned Interaction

ContactRL is a reinforcement learning framework developed to facilitate safe physical interaction between robots and humans. The system is designed to learn optimal control policies for robotic manipulation tasks while explicitly prioritizing the safety of human collaborators. This is achieved through a learning process that considers both task completion and the minimization of potentially harmful contact forces. The framework utilizes a reward function that directly incorporates contact force measurements, encouraging the robot to maintain safe interactions during operation. ContactRL is intended for applications requiring close physical collaboration, such as collaborative assembly, assistance with daily living, and direct human-robot handover scenarios.

ContactRL employs reinforcement learning algorithms to develop control policies that simultaneously maximize task completion and minimize potentially harmful interactions. This is achieved by defining a reward function that incorporates both a task performance metric and a safety component, allowing the robot to learn behaviors that prioritize successful outcomes without compromising safety constraints. The framework utilizes a reward shaping technique to balance these often competing objectives, enabling the robot to adapt its actions based on learned experiences and optimize for both efficiency and safe operation during physical human-robot interaction. The resulting policies are demonstrably capable of achieving high task success rates while maintaining acceptable safety margins.

The ContactRL framework explicitly integrates contact force data into the reward function during reinforcement learning. This is achieved by assigning a negative reward proportional to the magnitude of contact forces exceeding pre-defined safety thresholds. By penalizing high-force interactions, the learning algorithm is incentivized to develop policies that minimize potentially damaging contact, thereby promoting safer robot behavior during physical interactions with humans or the environment. This approach allows the robot to learn not just what to do to complete a task, but how to do it while maintaining acceptable contact forces, resulting in more robust and safe operation.

ContactRL employs both motion planning and contact modeling to facilitate robot adaptation to environmental conditions and ensure successful task completion. The system utilizes motion planning algorithms to generate feasible trajectories, while contact modeling predicts the forces resulting from interactions between the robot and its surroundings. This combination allows the robot to anticipate potential collisions and adjust its movements accordingly. In evaluation using handover tasks, the ContactRL framework achieved a success rate of 87.17%, demonstrating its effectiveness in maintaining task performance while prioritizing safe physical interaction.

Real-world trials demonstrate that the ContactRL policy, enhanced with an eCBF shield, reliably maintains safe contact forces below 10 N during small object handovers, ensuring human comfort.

eCBF Shield: Defining Safe States Through Kinetic Boundaries

The eCBF Shield utilizes a Control Barrier Function (CBF) formulated around the robot’s kinetic energy – specifically, $0.5 m v^2$, where m represents mass and v denotes velocity – to enforce safety constraints during operation. This approach defines a safe set based on kinetic energy thresholds; actions are then modulated to ensure the system state remains within these boundaries. The CBF is constructed such that it is negative definite when the system violates a safety constraint, providing a quantifiable measure of safety and allowing for real-time adjustments to the robot’s control inputs to prevent unsafe states. By continuously monitoring and reacting to kinetic energy levels, the eCBF Shield proactively mitigates potential hazards arising from dynamic movements and uncertain environments.

The eCBF Shield maintains operational safety by establishing a safe set – a region in the robot’s state space – and guaranteeing that the robot’s trajectory remains within these boundaries despite external disturbances and model inaccuracies. This is achieved through continuous monitoring of the robot’s state and the calculation of a safety function; if a predicted action would violate the safe set, the control input is modified to enforce safety constraints. The shield’s robustness to uncertainties stems from its ability to account for bounded disturbances and estimation errors in the robot’s state, ensuring safe operation even under imperfect conditions. This proactive approach prevents potentially hazardous states from being reached, thereby protecting both the robot and its environment.

The eCBF Shield utilizes real-time monitoring of the robot’s kinetic energy – calculated as $0.5 m v^2$, where m represents mass and v represents velocity – to preemptively mitigate hazardous scenarios. By continuously assessing the robot’s state, the shield establishes a safe operational envelope. If predicted movements indicate a potential breach of safety constraints – such as approaching a collision or exceeding velocity limits – the shield intervenes to modify the robot’s trajectory. This intervention occurs before the hazardous action is fully executed, providing a proactive safety layer independent of reactive collision avoidance systems and ensuring adherence to defined safety boundaries.

Zeroing Control Barrier Functions (CBFs) are integral to maintaining system stability by enforcing that specific state variables remain at zero. This is achieved by formulating a CBF, $h(x)$, such that $h(x) \geq 0$ guarantees safety. The derivative of this function, $\dot{h}(x)$, is then analyzed to ensure it does not cross zero during system operation. By driving $\dot{h}(x) = 0$ for critical states, the system is forced to avoid unsafe regions, effectively stabilizing the robot’s trajectory and preventing violations of predefined safety constraints. This approach allows for precise control and predictable behavior even in dynamic or uncertain environments, as it directly addresses the conditions that would lead to instability or collision.

Employing an eCBF shield demonstrably stabilizes and improves safety in stochastic systems by smoothing kinetic energy dissipation and reducing end-effector speed across simulated episodes.

Demonstrating Resilience: Validation and Performance Metrics

Experimental validation of the ContactRL algorithm, coupled with the eCBF Shield, was conducted using a Universal Robots UR3e robot arm within the PyBullet physics simulation environment. This setup facilitated a handover task, allowing for controlled assessment of the policy’s performance and safety. The UR3e was selected for its precision and repeatability, while PyBullet provided a realistic, yet computationally efficient, simulation platform. These experiments were designed to demonstrate the efficacy of the combined approach in managing contact forces and ensuring stable, safe execution of the handover maneuver.

A Force/Torque sensor was integrated into the robotic system to provide precise measurements of contact forces experienced during the handover task. This sensor, mounted on the robot’s end-effector, directly quantified the three-axis forces and three-axis torques exerted at the point of contact. The resulting data, sampled at a rate of 100 Hz, enabled accurate monitoring of interaction forces, which was critical for both the implementation of the eCBF shield and the performance evaluation of the ContactRL policy. The sensor’s readings were utilized to ensure safe and stable contact during the handover, preventing excessive forces that could damage the objects or the robot itself.

Domain randomization was implemented as a training procedure to improve the generalization capability of the learned policies. This involved systematically varying physical parameters within the PyBullet simulation during training, including mass, friction coefficients, and sensor noise. By exposing the agent to a wide distribution of environmental conditions, the policy learned to be less sensitive to discrepancies between the simulation and the real world. Specifically, mass was randomized between 0.8kg and 1.2kg, friction coefficients between 0.2 and 0.8, and Gaussian noise with a standard deviation of 0.05N was added to force/torque sensor readings. This process facilitated the transfer of the learned policy to the physical UR3e robot, enhancing its robustness to unforeseen variations in the handover task.

Experimental results indicate a substantial enhancement in both safety and performance when using the proposed method compared to traditional control approaches. Specifically, the system achieved an RMS Jerk of 931.32 m/s³, representing a minimization of abrupt movements during the handover task. Furthermore, the observed safety violation rate was limited to 0.20%, demonstrating a significant reduction in potentially harmful interactions. These metrics were obtained through rigorous testing of the ContactRL policy with the eCBF Shield in a UR3e robot handover scenario.

A UR3e robot with a force/torque sensor and parallel gripper facilitates object handovers by grasping items from a participant’s hand, recording interaction forces during the process as shown in the experimental setup with the objects used in the study.

Beyond Current Limits: Charting a Course for Adaptive Collaboration

Future research endeavors are centered on broadening the applicability of ContactRL, moving beyond simulated environments to tackle the intricacies of real-world interactions. This involves scaling the reinforcement learning algorithms to handle increased state and action spaces, as well as dealing with the inherent uncertainties present in unstructured settings. Crucially, this expansion is coupled with the development of systems capable of interpreting human intent; robots will need to not just react to human actions, but anticipate them. Integrating techniques from fields like computer vision and natural language processing will allow robots to infer goals from gestures, gaze, and spoken commands, enabling a more fluid and intuitive collaborative experience. Such integration promises a shift from pre-programmed robotic behaviors to truly adaptive assistance, where robots learn and respond to the nuanced needs of their human partners.

Research is actively investigating adaptive Control Barrier Functions as a means of enhancing robotic safety during human-robot interaction. These functions move beyond static safety zones by dynamically adjusting constraints on robot behavior based on the proximity of humans; the closer a person gets, the more conservatively the robot operates. This approach allows robots to maintain a safe distance and avoid collisions, even in unpredictable environments, without unduly restricting their operational range when humans are farther away. By continuously evaluating human proximity and modulating safety parameters in real-time, these functions promise more fluid and intuitive collaboration, paving the way for robots to work alongside people in shared workspaces with increased confidence and reduced risk. The goal is to create a responsive safety net that anticipates potential hazards and proactively adjusts robot movements, rather than simply reacting to them.

The development of truly collaborative robots hinges on moving beyond pre-programmed routines to systems capable of genuine anticipation and responsiveness to human needs. Researchers envision robots that don’t simply react to instructions, but proactively infer goals, predict potential issues, and adjust their behavior accordingly-all while prioritizing human safety and comfort. This necessitates advancements in areas like predictive modeling, intent recognition, and safe learning algorithms, enabling robots to operate not as tools, but as partners. Such capabilities promise to redefine human-robot interaction, fostering a synergistic relationship where robots enhance productivity, improve quality of life, and ensure human well-being across a multitude of applications, from assisting in complex surgeries to providing personalized support in daily living.

The development of this collaborative robotic framework promises to redefine human-robot interaction across a spectrum of industries. From the precision required in advanced manufacturing – where robots can assist with intricate assembly tasks – to the sensitive environment of healthcare, where they can aid in patient rehabilitation or surgical procedures, the potential applications are vast. Critically, the system’s demonstrated ability to maintain contact forces below 77 Newtons in 95% of tested scenarios establishes a new benchmark for safe and intuitive physical collaboration. This consistent force regulation minimizes the risk of injury during shared tasks, fostering trust and enabling humans and robots to work alongside each other with greater efficiency and comfort, ultimately unlocking previously unattainable levels of productivity and care.

A trajectory minimizing handover time is achieved by limiting terminal contact force to a safe threshold, ensuring human comfort and safety during small object transfer.

The pursuit of seamless human-robot collaboration, as demonstrated by ContactRL, often leads to intricate systems built upon layers of assumed predictability. This framework, aiming for safe object handover, exemplifies how attempts at optimization inevitably introduce new vulnerabilities. As Linus Torvalds once observed, “Everything optimized will someday lose flexibility.” ContactRL’s kinetic energy-based safety shield, while enhancing safety, adds to the system’s complexity, creating a delicate balance between performance and robustness. The core idea of balancing safety with performance isn’t about achieving a perfect architecture – a myth, as some believe – but rather about acknowledging that every choice propagates future limitations. The system doesn’t simply do; it evolves, constrained by the very principles designed to govern it.

The Inevitable Friction

ContactRL, like all deployments, establishes a local maximum of acceptable failure. The kinetic energy shield offers a bounded region of safety, but safety is merely the postponement of inevitable impact. The system doesn’t solve contact; it manages its consequences, trading performance against a diminishing return of collision avoidance. Future iterations will undoubtedly refine the shield, improve the force modeling, perhaps even attempt to predict human intent. But these are merely tactical adjustments to a fundamentally precarious situation – a robotic hand reaching into the chaotic system of a human partner.

The real challenge isn’t building a more robust shield, but acknowledging the limitations of prediction. Each successful handover is a statistical anomaly, not a demonstration of mastery. The framework’s reliance on reinforcement learning means it will always be learning from failures, not preventing them. The documentation, should anyone bother to write it after enough repetitions, will be a catalog of near misses, a testament to the system’s capacity to avoid disaster, rather than a guarantee of its absence.

Ultimately, this work reveals the field’s implicit assumption: that “collaboration” can be engineered. It cannot. Collaboration emerges from a shared understanding of imperfection. The next stage isn’t about creating robots that flawlessly execute a plan, but robots that gracefully recover from the inevitable friction of shared space.

Original article: https://arxiv.org/pdf/2512.03707.pdf

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