Give Robots a Shove: New Benchmark Drives Progress in Pushing-Based Navigation

Author: Denis Avetisyan

Researchers have unveiled a standardized testing suite to accelerate the development of robots that can effectively navigate and manipulate objects through pushing.

The study constructs progressively complex navigational challenges-ranging from maze traversal with movable obstacles to autonomous ship routing through icy waters, and robotic manipulation tasks involving box delivery and area clearing-to explore the limits of pushing-based navigation and manipulation in both two and three-dimensional simulations, acknowledging that each increment in complexity foreshadows inevitable systemic failure.

Bench-Push offers customizable simulation environments, novel metrics, and baseline algorithms for benchmarking pushing-based navigation and manipulation tasks for mobile robots, enabling robust sim-to-real transfer.

Traditional robotics often struggles in cluttered environments requiring interaction with movable objects, hindering deployment in realistic scenarios. To address this, we introduce Bench-Push: Benchmarking Pushing-based Navigation and Manipulation Tasks for Mobile Robots, a unified and extensible benchmark suite designed to standardize the evaluation of pushing-based mobile robot algorithms. Bench-Push provides diverse simulated environments, novel metrics for assessing performance beyond simple success rates, and baseline implementations to facilitate reproducible research. Will this benchmark accelerate the development of more robust and adaptable mobile robots capable of navigating and manipulating complex, dynamic spaces?

The Illusion of Separation: Navigation and Manipulation

Historically, robotic systems have been designed with a distinct separation between how they move through space – navigation – and how they interact with objects – manipulation. This compartmentalization, while simplifying initial development, creates a significant bottleneck in real-world applications. Robots struggle when faced with scenarios demanding the simultaneous consideration of both tasks; for example, a robot tasked with retrieving an object from a cluttered room must not only plan a path to the object but also adjust that path dynamically based on how grasping the object will affect its subsequent movement. This inability to fluidly integrate navigation and manipulation limits a robot’s adaptability, forcing pre-programmed responses to specific situations rather than genuine problem-solving in the face of unpredictable environments. Consequently, robots often fail when confronted with even minor deviations from their expected operating conditions, highlighting the need for a more holistic approach to robotic intelligence.

Robots operating in the real world face a constant stream of unforeseen changes – shifting objects, varying lighting, and unpredictable human interactions. Consequently, a robot’s ability to navigate and manipulate objects cannot be treated as isolated tasks; instead, these capabilities must be seamlessly integrated for truly robust performance. A robot that can successfully map a room, for example, must also be able to adjust its path if an object is moved during navigation, or deftly grasp an item while simultaneously avoiding obstacles. This demand for unified intelligence necessitates algorithms that allow a robot to reason about both its physical location and the objects within its environment, enabling it to adapt to dynamic scenarios and maintain functionality even when faced with the unexpected complexities of a real-world setting.

Current robotic evaluation benchmarks often present navigation and manipulation challenges in isolation, failing to capture the intricate interplay required for real-world success. These simplified assessments typically prioritize speed or accuracy within a single task, overlooking a robot’s ability to dynamically integrate both skills – for example, navigating to grasp a moving object or re-planning a path while manipulating an item. Consequently, a robot may excel on isolated tests but falter in dynamic, unstructured environments where seamless coordination is paramount. The lack of complex, integrated benchmarks thus hinders progress in embodied artificial intelligence, as it provides an incomplete picture of a robot’s true capabilities and limits the development of algorithms capable of genuine adaptability and robust performance. A truly intelligent robot must not simply navigate and manipulate, but navigate while manipulating, and vice versa.

Teleoperated maze path performance reveals a trade-off between efficiency and collision avoidance, with the shortest path maximizing efficiency but incurring collisions, the longest path being collision-free but inefficient, and a balanced path potentially offering the optimal compromise.

Bench-Push: A Controlled Collapse of Categorization

Bench-Push addresses the lack of standardized evaluation for algorithms focused on pushing-based mobile robot tasks. Current research in this area suffers from inconsistent environment setups, task definitions, and performance metrics, hindering meaningful comparisons between different approaches. Bench-Push provides a unified platform with precisely defined scenarios, robot models, and evaluation protocols. This standardization facilitates reproducible research and allows for objective assessment of algorithm performance across a range of pushing challenges, ultimately accelerating progress in the field of mobile manipulation and robotic task planning.

Bench-Push environments necessitate the simultaneous execution of navigation and manipulation skills, presenting a challenge beyond either capability in isolation. These simulated environments feature complex layouts with obstacles and target objects requiring both path planning and precise robotic control. Algorithms are required to coordinate these actions to successfully navigate to an object and then manipulate it – for example, pushing it to a goal location – within the same episode. This integration forces the development of algorithms capable of learning temporally extended behaviors and handling the inherent dependencies between navigation and manipulation tasks, thus evaluating a robot’s ability to solve complete, real-world pushing problems.

Bench-Push utilizes the Gymnasium framework to provide a standardized and accessible interface for researchers developing reinforcement learning algorithms for mobile manipulation. This integration simplifies the process of training and evaluating algorithms within realistic, complex environments. By adhering to the Gymnasium API, Bench-Push ensures compatibility with a wide range of existing reinforcement learning tools and libraries, and enables direct comparison of performance across diverse approaches, including those employing different state representations, action spaces, and learning algorithms. The platform’s design streamlines experimentation and facilitates reproducible research in pushing-based robotic tasks.

Diverse Trials: A Gradient of Algorithmic Stress

Bench-Push utilizes a suite of environments – Area-Clearing, Box-Delivery, Maze, and Ship-Ice – designed to provide a comprehensive evaluation of algorithm performance across diverse challenges. Area-Clearing tasks agents with navigating and removing obstacles within a defined space, while Box-Delivery requires agents to locate, pick up, and deliver boxes to target locations. The Maze environment presents a navigation problem focused on pathfinding and efficient traversal of complex layouts. Ship-Ice simulates a physics-based environment where agents must control a ship on ice, demanding precise control and strategic maneuvering. These environments collectively assess an algorithm’s capabilities in areas such as navigation, object manipulation, path planning, and dynamic control.

Bench-Push environments present a gradient of algorithmic challenges, extending beyond basic navigation. Simpler scenarios, such as Area-Clearing, primarily require reactive obstacle avoidance and pathfinding capabilities. Conversely, environments like Maze and Ship-Ice necessitate more sophisticated planning and sequential decision-making. Box-Delivery, in particular, demands algorithms capable of strategic resource management, task prioritization, and long-horizon planning to efficiently transport objects while navigating dynamic obstacles and potentially interacting with other agents. The complexity of these problems scales with the environment’s size, the number of agents, and the presence of dynamic elements, effectively testing an algorithm’s capacity for both reactive and proactive behavior.

Bench-Push leverages both high-fidelity and lightweight physics engines to facilitate a comprehensive testing process. MuJoCo is employed when realistic simulation of dynamics and contact forces is required, enabling evaluation of algorithms in complex, physically plausible scenarios. Conversely, Pymunk provides a computationally efficient alternative for rapid prototyping and large-scale experimentation, allowing for quicker iteration and testing of core algorithmic logic without the overhead of detailed physical modeling. This dual-engine approach balances simulation accuracy with computational cost, supporting both detailed performance analysis and broad exploration of algorithmic designs.

The Maze environment was implemented across 2D simulation, 3D simulation, and a physical testbed for comprehensive validation.

Establishing Metrics: Quantifying the Inevitable Failure

Bench-Push incorporates well-known reinforcement learning algorithms, specifically Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC), to provide standardized baselines for evaluating novel approaches to robotic manipulation. These algorithms serve as comparative benchmarks against which the performance of newly developed methods can be quantitatively assessed. Utilizing established algorithms ensures objective comparisons and facilitates a clear understanding of the improvements or trade-offs introduced by new techniques. The integration of PPO and SAC allows for a robust and reproducible evaluation framework within the Bench-Push environment.

Performance evaluation within Bench-Push utilizes three primary quantitative metrics to provide a comprehensive assessment of algorithm efficacy. Task Success Rate measures the percentage of trials where the agent successfully completes the designated task. Efficiency Score quantifies the optimality of the solution path, calculated as the ratio of the shortest possible path length to the actual path length taken; lower scores indicate greater efficiency. Finally, Interaction Effort Score represents the cumulative distance traveled by the end-effector during task execution, serving as a proxy for the physical strain or energy expenditure required to complete the task. These metrics, considered in conjunction, allow for a robust comparison of algorithm performance across varying environmental complexities and task requirements.

Bench-Push evaluations have consistently demonstrated a high degree of correlation between algorithm performance in simulated environments and subsequent performance when deployed on physical robotic systems. This sim-to-real transferability was assessed by running identical experiments – measuring Task Success Rate, Efficiency Score, and Interaction Effort Score – in both simulation and on a physical robot, and comparing the resulting metrics. Statistical analysis of these comparative runs indicates negligible performance differences, confirming the validity of simulation as a proxy for real-world experimentation and reducing the need for extensive and costly physical testing during algorithm development.

Performance evaluations within the Maze environment consistently demonstrated that the Proximal Policy Optimization (PPO) algorithm achieved higher Efficiency Scores compared to both the Soft Actor-Critic (SAC) algorithm and the Rapidly-exploring Random Tree (RRT) planner. Conversely, in the Box-Delivery environment, the Scalable Algorithm for Motion (SAM) outperformed both PPO and SAC based on the same Efficiency Score metric. These results indicate a performance disparity between algorithms dependent on the specific task environment, suggesting that algorithm selection should be tailored to the characteristics of the robotic manipulation or navigation challenge.

Interaction Effort Scores, a metric quantifying the number of actions required to complete a task, demonstrated a negative correlation with obstacle density in both the Maze and Box-Delivery environments. Specifically, as the number of obstacles increased, the Interaction Effort Score decreased, indicating a statistically significant increase in task difficulty. This suggests that algorithms required more actions to navigate or manipulate objects within increasingly cluttered spaces, confirming that obstacle density directly impacts the computational burden and physical demands placed on robotic systems performing these tasks. This trend was consistent across multiple trials and algorithms tested, providing robust evidence for the impact of environmental complexity on performance.

Teleoperated paths for box removal demonstrate a trade-off between efficiency and effort, with the red path being most efficient but requiring longer movements, the blue path minimizing movement but maximizing path length, and the yellow path potentially offering the optimal balance.

Towards Inevitable Collapse: A Future of Controlled Degradation

The challenge of transferring algorithms from simulated environments to physical robots – known as Sim-to-Real transfer – is significantly addressed by Bench-Push. This platform provides a controlled and standardized setting for developing and evaluating algorithms trained in simulation, then deployed onto real robotic hardware. By offering a diverse set of pushing scenarios and a consistent evaluation framework, Bench-Push enables researchers to isolate and improve the robustness of their algorithms, bridging the gap between idealized simulation and the complexities of the physical world. This focused approach allows for more reliable and efficient development of robotic systems capable of operating effectively in unpredictable, real-world conditions, ultimately accelerating progress in fields like logistics, search and rescue, and in-home assistance.

The ability for a robot to effectively exert force and manipulate objects through pushing is fundamental to a surprisingly broad range of practical applications. Beyond the increasingly automated environments of modern warehouses, where robots routinely nudge and organize inventory, pushing-based locomotion and manipulation offer unique advantages in unstructured or disaster-stricken areas. In these scenarios, wheeled or legged robots can utilize pushing to navigate cluttered spaces, clear debris, and even remotely interact with potentially hazardous materials. This approach bypasses the need for precise grasping – a capability often compromised by unpredictable environments or damaged objects – and instead relies on the physics of interaction to achieve complex tasks. Consequently, advancements in pushing-based techniques promise to significantly enhance robotic adaptability and effectiveness in challenging real-world settings, extending beyond logistics into crucial areas like search and rescue and environmental remediation.

The development of Bench-Push represents a significant step towards streamlining research in embodied artificial intelligence and robotics through the establishment of a standardized platform. Prior to its creation, comparing algorithms across different robotic systems and simulation environments proved challenging due to inconsistencies in hardware, software, and evaluation metrics. This platform offers a unified environment, allowing researchers to readily share code, datasets, and experimental results, thereby fostering collaboration and accelerating the pace of innovation. By removing barriers to reproducibility and enabling direct comparisons, Bench-Push isn’t simply a tool for evaluation; it functions as a catalyst, driving progress in areas like pushing-based navigation and manipulation, and ultimately facilitating the translation of algorithms from simulated environments to real-world applications with greater efficiency and reliability.

The pursuit of standardized benchmarks, as exemplified by Bench-Push, echoes a cyclical pattern inherent in all complex systems. The effort to define metrics and baseline implementations isn’t about achieving ultimate control, but rather about establishing a shared language for observation and iterative refinement. As Donald Knuth observed, “The best computer costs nothing-it’s the one you already have.” Similarly, this benchmark isn’t about creating the ‘perfect’ evaluation; it’s about leveraging existing tools and knowledge to foster progress. Each iteration of the benchmark, each algorithm tested, represents a promise made to the past, building upon prior work while inevitably revealing new limitations and pathways for growth. The system, in effect, begins fixing itself, as researchers respond to the challenges laid bare by the evaluation suite.

What Lies Ahead?

Bench-Push formalizes the mechanics of robotic persuasion – the art of moving the world with a nudge, rather than a grasp. This is not merely a shift in actuation, but a confession. The confession that robots, like all agents, will always be entangled in dependencies. The benchmark isolates pushing as a problem, but the true challenge lies in the choreography of these interactions. A robot that masters pushing within a controlled environment has only delayed the inevitable complexity of real-world entanglement.

The suite’s metrics, while valuable, measure efficiency within the defined task. They do not, and cannot, account for the emergent behaviors that arise when these systems are layered – when a pushing robot must also navigate a crowded space, negotiate with other agents, or adapt to unexpected changes in its environment. Each added layer of abstraction is a new surface for failure, a new point where the carefully constructed order yields to the unpredictable currents of the real world.

The pursuit of sim-to-real transfer remains, as always, a hopeful delusion. The gap isn’t one of fidelity, but of fundamental difference. Simulation offers a temporary reprieve from chaos, but it cannot inoculate against it. The system will eventually encounter a configuration, a friction coefficient, an unexpected object, that reveals the fragility of its assumptions. It splits the problem, but not the fate.

Original article: https://arxiv.org/pdf/2512.11736.pdf

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