When uncertainty is critical to success.

Empirical distributions and filters.

Log uncertainties and simulate edge cases.

Tune gains and safety constraints.

Fuse modalities to reduce uncertainty.

Robotics Topic 18: Manipulation under Uncertainty |…

Q: Exploration vs exploitation?

Use info-gathering actions judiciously.

Q: Recovery?

Design re-plans and safe retreats.

Q: Training?

Domain randomization for policies.

Q: Metrics?

Success under disturbances and noise.

Belief and Planning

Belief-space planning and POMDPs (Partially Observable Markov Decision Processes) provide a framework for decision making when the agent’s environment is uncertain. These methods explicitly represent uncertainty in the agent's knowledge of the world, allowing it to reason about potential outcomes based on incomplete information. POMDPs are particularly useful when actions affect both the state of the environment and the agent’s observation of that state, creating a complex feedback loop.

Information gathering actions play a crucial role in reducing uncertainty within a manipulation task. By strategically selecting actions designed to reveal more about the environment – such as adding sensors or requesting additional data – agents can refine their belief states and improve the accuracy of their plans. Careful design of these actions is key to balancing exploration with exploitation.

Contact-rich strategies are often employed in manipulation under uncertainty, focusing on maximizing the information gained from each interaction with the environment. These approaches prioritize actions that provide detailed feedback about object properties, positions, and potential obstacles, enabling more precise control and reducing the risk of unexpected events.

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Robust Control

Impedance control, funneling, and policies robust to disturbances are essential techniques for achieving reliable manipulation in uncertain environments. Impedance control allows agents to maintain a desired force or torque relationship with objects, mitigating the effects of external forces and unexpected interactions. Funneling strategies guide the agent towards a target while minimizing collisions and maximizing stability.

Robust control policies are designed to perform effectively despite unpredictable disturbances and variations in environmental conditions. These policies typically incorporate safety margins and adaptive mechanisms that adjust behavior based on real-time feedback, ensuring continued progress even when faced with unexpected challenges. Careful tuning of these policies is crucial for optimal performance.

Example

Example: Insertion with Uncertain Hole Pose – this scenario highlights the need for uncertainty management during a complex manipulation task. The agent must accurately estimate the position and orientation of a hole in an object, which is inherently uncertain due to factors like imprecise measurements or variations in material properties.

Estimate pose with uncertainty – the agent utilizes sensor data and potentially probabilistic models to quantify the uncertainty associated with its pose estimation. This involves employing techniques like Kalman filtering or Bayesian inference to generate a probability distribution representing the possible locations of the hole, rather than a single point estimate.

Plan funneling and force-limited insertion – based on the estimated uncertainty, the agent plans a controlled insertion strategy using funneling to guide the object towards the hole. Simultaneously, it applies a limited force to ensure smooth movement and prevent excessive stress on the object or the environment.

Frequently asked questions

When to use POMDPs?

POMDPs are most appropriate when uncertainty significantly impacts the success of a manipulation task. If the agent’s perception is noisy, actions affect both state and observation, or there's inherent randomness in the environment, then a POMDP-based approach will provide a more robust solution compared to simpler planning methods.

How to model noise?

Noise can be modeled using empirical distributions derived from simulated data or by employing filters like Kalman filters. These filters estimate the state of the environment based on noisy sensor readings, providing a more accurate representation of the agent’s belief than simply assuming perfect knowledge.

How to debug?

Debugging manipulation under uncertainty requires careful logging of uncertainties at each step and simulating a wide range of edge cases. This allows you to identify potential failure modes, assess the sensitivity of your policies to noise, and refine your models for more accurate predictions.

Exploration vs exploitation?

Balancing exploration (gathering new information) and exploitation (using current knowledge to achieve goals) is crucial. Use info-gathering actions judiciously – prioritize exploring areas where uncertainty is highest or where previous attempts have yielded limited results, but avoid excessive exploration that detracts from progress.

Force control?

Fine-tuning gains and incorporating safety constraints within your force control system is essential. Adjusting the gains allows you to modulate the responsiveness of the controller, while carefully defined safety constraints prevent excessive forces that could damage objects or the environment.

Recovery?

Design re-plans and safe retreats as part of your overall strategy. If an action leads to a significant increase in uncertainty or a deviation from the desired outcome, trigger a re-plan that attempts a different approach, or implement a controlled retreat to a safer state.

Training?

Domain randomization for policies can significantly improve robustness and generalization. By training agents in a diverse range of simulated environments with varying parameters (e.g., friction coefficients, object shapes), you create more adaptable policies that are less sensitive to specific conditions.

Sensors?

Fusing modalities – combining data from multiple sensors (e.g., visual and tactile) – is a powerful way to reduce uncertainty. By integrating complementary information, the agent can create a more complete picture of its surroundings and improve the accuracy of its perception.

Latency?

Bound delays and compensate for latency in sensor readings and actuator responses. Accurate estimation of these delays is critical for effective control, as even small latencies can accumulate over time and lead to significant errors.

Metrics?

Success under disturbances and noise should be your primary metric. Evaluate the agent’s ability to achieve its goals despite external perturbations and variations in sensor data, providing a realistic measure of its robustness and performance.

Try it live

Everything above runs in your browser — open Inverse Kinematics (FABRIK) and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

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