Robots That ‘Understand’ Physics Get a Boost with ManiDreams

Author: Denis Avetisyan

A new open-source library empowers robots to handle objects more reliably by accounting for real-world uncertainties in physics and perception.

ManiDreams integrates uncertainties in perception, parameters, and structure through Task-specific Intuitive Physics and Domain-randomized Instance Sets, enabling a system to navigate standard manipulation tasks-such as pushing and picking-with robustness achieved by simulating varied instances and leveraging learned physical intuition.

ManiDreams unifies uncertainty-aware manipulation through composable abstractions, enhancing robustness in robotic manipulation planning and execution.

Despite advances in robotic manipulation, real-world performance often degrades due to inherent uncertainties that are rarely explicitly addressed within planning loops. This work introduces ManiDreams: An Open-Source Library for Robust Object Manipulation via Uncertainty-aware Task-specific Intuitive Physics, a modular framework designed to integrate perceptual, parametric, and structural uncertainties through composable abstractions. By employing a sample-predict-constrain loop, ManiDreams enhances robustness without requiring policy retraining, effectively propagating uncertainty through dynamics prediction and declarative constraint specification. Does this approach represent a critical step towards deploying more reliable and adaptable robotic systems in unstructured environments?

The Inevitable Drift: Embracing Uncertainty in Robotic Control

Conventional robotic manipulation strategies are fundamentally predicated on the availability of accurate environmental models and precise kinematic and dynamic representations of the robot itself. However, the real world rarely conforms to these idealized assumptions. Minute discrepancies – whether in the perceived location of an object, the friction between surfaces, or even subtle variations in an object’s shape – accumulate and propagate through the robot’s control systems. These uncertainties present a significant challenge, as even seemingly insignificant errors can lead to failed grasps, collisions, or inaccurate task completion. The reliance on perfect knowledge, therefore, limits a robot’s ability to function reliably in dynamic, cluttered, or unpredictable environments, highlighting the need for more robust and adaptable manipulation techniques.

Robot manipulation frequently falters not due to mechanical limitations, but because of inherent uncertainties in perceiving and interacting with the world. These uncertainties manifest in three key forms: perceptual, where sensor data is noisy or incomplete; parametric, relating to inaccuracies in known object properties like weight or friction; and structural, stemming from unpredictable variations in an object’s shape or internal composition. Consequently, even with sophisticated algorithms, a robot’s predicted outcome of an action often diverges from reality, leading to failed grasps, collisions, or incomplete task execution. The accumulation of these prediction errors presents a significant barrier to deploying robots in dynamic, real-world environments where perfect information is simply unattainable, necessitating strategies for robust adaptation and error recovery.

For robots to move beyond controlled settings and operate effectively in the real world, a fundamental shift in manipulation strategies is necessary. Unstructured environments – homes, factories, disaster zones – present inherent unpredictability, from variations in object properties to unforeseen obstacles. Robust and reliable robotic operation hinges on the ability to navigate these uncertainties; systems reliant on precise, pre-programmed models will inevitably falter. Addressing these challenges requires developing algorithms and hardware that allow robots to adapt in real-time, leveraging sensory feedback and probabilistic reasoning to account for imperfect information and unexpected events. This adaptability isn’t merely about preventing failures, but enabling robots to perform complex tasks with a level of resilience comparable to human dexterity, ultimately unlocking their potential in dynamic and unpredictable scenarios.

Using learning-based TSIP and pixel-based DRIS, ManiDreams successfully plans and executes both cluttered push-picking and wall-corner scooping of diverse objects, as demonstrated in real-robot deployment.

ManiDreams: A Framework for Anticipating the Unforeseen

ManiDreams departs from traditional robot manipulation planning by directly incorporating uncertainty representation and propagation into the planning process. Existing methods often treat uncertainty as a post-hoc correction or rely on simplified probabilistic models. In contrast, ManiDreams employs a belief space approach, where the robot’s state is represented as a probability distribution rather than a single point estimate. This distribution is updated and refined throughout the planning horizon, allowing the system to anticipate potential errors arising from sensor noise, imprecise robot models, and unpredictable environmental factors. The framework achieves this by explicitly modeling uncertainty in both the robot’s state and the environment, and then propagating these uncertainties forward through the predicted trajectories, enabling more robust and reliable manipulation strategies.

The Sample-Predict-Constrain paradigm forms the core of ManiDreams’ planning process. Initially, a diverse set of possible trajectories are sampled from the robot’s action space. These trajectories are then fed into a learned prediction model, which estimates the resulting state of the environment and associated uncertainty. Finally, a constraint optimization process constrains the sampled trajectories to those predicted to achieve the desired goal while respecting both robot dynamics and predicted environmental uncertainties, effectively filtering out high-risk or infeasible plans. This iterative process of sampling, prediction, and constraint allows the framework to proactively account for uncertainty and generate robust manipulation plans.

ManiDreams addresses uncertainty in robotic manipulation by integrating three distinct sources – perceptual, parametric, and structural – within a unified planning framework. Perceptual uncertainty stems from sensor noise and limitations in state estimation; parametric uncertainty arises from inaccuracies in robot models and physical parameters; and structural uncertainty accounts for unpredictable environmental factors like deformable objects or incomplete knowledge of object properties. The framework represents each uncertainty type as a probability distribution and propagates these distributions through the Sample-Predict-Constrain planning process, allowing for robust motion planning even with significant uncertainty in the robot’s knowledge of its environment and itself. This unified approach contrasts with prior methods that typically address each uncertainty source in isolation, leading to more complex and less generalizable solutions.

ManiDreams iteratively refines manipulation plans by alternating between a planning loop-which generates, predicts, and selects actions based on a dynamic representation of the scene [latex]DRIS[/latex]-and an execution loop that dispatches actions to a robot or simulator and updates the [latex]DRIS[/latex] based on observed outcomes.

Propagating the Inevitable: Simulation and Learned Models

Task-Specific Intuitive Physics (TSIP) serves as the computational layer responsible for predicting the outcomes of actions within a defined task. It operates on distributional states, representing a probability distribution over possible world states, and propagates these distributions forward in time given a proposed action. This propagation is not a single-point estimate, but rather a full distribution reflecting the inherent uncertainty in the environment and the action’s effect. The interface provided by TSIP allows the agent to evaluate potential actions by assessing the likely future states and associated rewards, enabling planning and decision-making under uncertainty. Essentially, TSIP transforms an action and a distributional state into a new distributional state representing the predicted consequences of that action.

Task-Specific Intuitive Physics (TSIP) propagates distributional states through candidate actions using one of two primary methods. Simulation-Based TSIP leverages a pre-existing physics simulator to predict the outcome of actions, relying on explicitly defined physical laws. Alternatively, Learning-Based TSIP employs a learned world model, trained on observational data, to forecast future states. This allows the system to handle dynamics where a traditional physics simulator may be unavailable or computationally expensive, providing a data-driven approach to state propagation.

The Learning-Based Task-Specific Intuitive Physics (TSIP) approach employs a Diffusion Model for state prediction, representing a data-driven alternative to physics simulation. Diffusion Models are generative models trained on datasets of state transitions; given a current state and an action, the model iteratively refines a prediction of the subsequent state by denoising a random variable. This process allows the system to learn complex, potentially non-analytic dynamics directly from observed data, without requiring explicit specification of physical laws or parameters. The learned model then propagates distributional states by repeatedly applying the diffusion process under candidate actions, enabling probabilistic reasoning about future outcomes.

Both simulation-based and diffusion model-based trajectory simplification policies (TSIPs) successfully guide object pushing along circular trajectories using randomly selected objects from the YCB dataset.

Constraining Action: Accepting the Limits of Prediction

Caging Constraints function by limiting the permissible state space of a dynamic system, effectively bounding the potential for divergence caused by accumulated uncertainty in the distributional state. As a system operates and predictions are made, errors can compound, leading to increasingly inaccurate state estimates and potentially catastrophic failures – such as collisions or instability. By defining boundaries or “cages” within the state space, these constraints prevent the system from reaching configurations that would result in such failures, even in the presence of significant uncertainty. This is achieved by rejecting actions that would project the system state outside of these defined boundaries, thereby ensuring continued safe and stable operation, particularly in complex or unpredictable environments.

Geometric Caging Constraints function by defining a permissible region within the state space based on the physical dimensions and limitations of the environment and the controlled object. This implementation restricts the predicted state of the object to remain within these geometrically defined boundaries during the forward model simulation. Specifically, the constraint enforces limits on position, orientation, and velocity, preventing the object from occupying invalid or physically impossible configurations. This is achieved by modifying the state vector proposed by the forward model if it violates these geometric bounds, effectively ‘caging’ the object’s predicted trajectory within a safe and feasible space.

The Solver component functions as the central decision-making module, integrating three key elements to determine optimal actions. It utilizes the Time-Series Importance Prediction (TSIP) forward model to predict the consequences of potential actions, then applies caging constraints to limit the action space and prevent unstable or unsafe states. Finally, the Solver employs one of three optimization algorithms – the Model Predictive Path Integral (MPPI) Optimizer, the NN-best Selector, or the Proximal Policy Optimization (PPO) Baseline – to evaluate possible actions based on the TSIP predictions and caging constraints, ultimately selecting the action predicted to yield the most favorable outcome.

The sample-predict-constrain pipeline is configured and launched in three steps, and reducing it to direct policy execution by setting [latex]num_samples = 1[/latex] and omitting the cage provides a baseline for experimental comparisons.

Beyond Simulation: Embracing Reality and Charting Future Directions

The ManiDreams framework extends beyond simulation through its Real-World Executor component, designed to bridge the gap between virtual training and physical robot execution. This component handles the complexities of translating learned policies into actionable commands for a real robot, managing communication protocols and ensuring proper synchronization between software and hardware. Crucially, it incorporates mechanisms for real-time feedback and error recovery, allowing the robot to adapt to unforeseen circumstances during operation. By abstracting away low-level control details, the Executor enables researchers and developers to focus on higher-level task planning and learning, ultimately accelerating the deployment of sophisticated robotic capabilities in real-world environments.

The ManiDreams framework leverages the capabilities of the Maniskill3 simulator to substantially accelerate both training and evaluation phases prior to real-world deployment. This virtual environment allows for extensive experimentation with various scenarios and perturbations – including observation noise and physics variations – without the time and resource constraints of physical robotics. By conducting the bulk of the learning process within Maniskill3, the framework minimizes the need for costly and potentially damaging trials on the actual robot, facilitating a more efficient and robust development cycle. This simulated pre-training not only reduces the risk associated with initial deployments but also significantly speeds up the overall learning process, ultimately enabling the robot to adapt more readily to unforeseen circumstances in real-world environments.

The ManiDreams framework enhances generalization to novel environments through the utilization of a Domain-Randomized Instance Set (DRIS) to represent distributional states. Rather than training on a single, fixed environment, the system learns to anticipate a range of possible scenarios by sampling from a diverse collection of simulated instances. This approach effectively expands the training distribution, allowing the robot to develop robust policies that are less susceptible to unexpected changes in physics, object properties, or sensor readings. By experiencing a wider variety of conditions during training, the framework cultivates an internal representation of uncertainty, enabling it to adapt more readily to unseen situations and maintain reliable performance even when faced with significant perturbations or noise – a crucial capability for real-world robotic applications.

Rigorous experimentation reveals the ManiDreams framework achieves an impressive success rate of up to 82% when utilizing a medium-width distributional state representation via the Domain-Randomized Instance Set (DRIS). This performance demonstrably exceeds that of a standard Proximal Policy Optimization (PPO) baseline, highlighting the effectiveness of the framework’s approach to generalization. By training across a diverse set of simulated physics parameters-captured by the DRIS-the system exhibits a robust ability to adapt to previously unseen conditions in the physical world. The observed gains suggest that representing uncertainty in the environment through distributional states is crucial for building robotic systems capable of reliable operation beyond the limitations of narrow training distributions.

ManiDreams demonstrates robust performance across a suite of challenging robotic manipulation tasks-including pushing and picking cubes-even when subjected to realistic environmental disturbances. Testing reveals substantial gains in success rates as observation noise, communication delays, and unpredictable physics variations are incrementally increased. This resilience stems from the framework’s ability to learn policies that are not overly sensitive to minor imperfections in sensor readings or robot calibrations, and its capacity to adapt to unexpected shifts in the physical world. The observed improvements suggest a pathway toward deploying robotic systems in less controlled and more dynamic real-world settings, where consistent performance demands adaptability beyond the capabilities of traditional reinforcement learning approaches.

While the ManiDreams framework demonstrates substantial gains in task success and robustness, its computational demands represent a key consideration for real-time applications. Evaluations reveal a runtime overhead of 77.9 milliseconds per step when utilizing 16 solver samples and 8 Domain-Randomized Instance Set (DRIS) instances – a noticeable increase compared to the 20.2 milliseconds required by the baseline Proximal Policy Optimization (PPO) algorithm. This added latency stems from the framework’s sophisticated approach to distributional state representation and its reliance on multiple solver samples to navigate uncertain environments. Ongoing research is focused on optimizing these computational processes, exploring techniques such as reduced sampling and parallelization, to minimize the performance gap and enable broader deployment on resource-constrained robotic platforms.

This three-layer architecture decouples core motion planning abstractions ([latex] ext{StateSpace, StatePropagator, StateValidityChecker, Planner}[/latex]) from environment-specific implementations and user-defined tasks, simplifying the addition of new problems.

The pursuit of robust robotic systems, as demonstrated by ManiDreams, echoes a fundamental truth about all complex endeavors. Systems, whether mechanical or mathematical, are perpetually subject to the unpredictable nature of real-world interactions. ManiDreams’ approach to unifying uncertainties-perceptual, parametric, and structural-acknowledges this inherent fragility. As Paul Erdős once stated, “A mathematician knows a lot of things, but a physicist knows everything.” This sentiment reflects the necessity of anticipating every possible variable, a task ManiDreams addresses through its composable abstractions. The framework doesn’t attempt to eliminate uncertainty, but rather to learn to navigate it, allowing systems to age gracefully even amidst unpredictable conditions. Observing the intricate dance between prediction and constraint, a core element of ManiDreams, often proves more valuable than striving for an unattainable perfection.

What’s Next?

ManiDreams, as a framework for navigating the inherent stochasticity of the physical world, represents a temporary caching of inevitable decay. The unification of uncertainty – perceptual, parametric, structural – is not a solution, but a postponement. Systems built upon distributional dynamics will, with sufficient time, encounter distributions previously unseen, and the carefully constructed abstractions will begin to fray. The question isn’t whether failures will occur, but when, and how gracefully the system degrades.

Future work will inevitably focus on extending the scope of uncertainty modeled. However, a more pressing challenge lies in acknowledging the limits of prediction. The pursuit of ‘robustness’ often implies a static target, while reality is a relentlessly shifting landscape. Research should consider methods for adaptive uncertainty, systems that learn not just to predict, but to anticipate their own predictive failures – to build in the latency of acknowledgement as a core design principle.

Ultimately, the true test of ManiDreams, and frameworks like it, will not be in achieving peak performance under controlled conditions, but in demonstrating a measured acceptance of entropy. Uptime is a fleeting illusion. The long game isn’t about preventing falls, but about designing systems that can recover, re-orient, and continue functioning – however imperfectly – in the face of inevitable systemic decline.

Original article: https://arxiv.org/pdf/2603.18336.pdf

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