Multi-Modal MPPI and Active Inference for Reactive Task and Motion Planning
(2312.02328)Abstract
Task and Motion Planning (TAMP) has made strides in complex manipulation tasks, yet the execution robustness of the planned solutions remains overlooked. In this work, we propose a method for reactive TAMP to cope with runtime uncertainties and disturbances. We combine an Active Inference planner (AIP) for adaptive high-level action selection and a novel Multi-Modal Model Predictive Path Integral controller (M3P2I) for low-level control. This results in a scheme that simultaneously adapts both high-level actions and low-level motions. The AIP generates alternative symbolic plans, each linked to a cost function for M3P2I. The latter employs a physics simulator for diverse trajectory rollouts, deriving optimal control by weighing the different samples according to their cost. This idea enables blending different robot skills for fluid and reactive plan execution, accommodating plan adjustments at both the high and low levels to cope, for instance, with dynamic obstacles or disturbances that invalidate the current plan. We have tested our approach in simulations and real-world scenarios.
Overview
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The paper introduces a reactive Task and Motion Planning (TAMP) method combining an Active Inference planner (AIP) and a Multi-Modal Model Predictive Path Integral controller (M3P2I).
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The AIP is responsible for generating high-level decision-making action plans based on the current state and possible actions.
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The M3P2I evaluates multiple alternative plans by sampling different possible actions and computing trajectories using a physics simulator.
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The method has been tested with robotic manipulators and proved successful in push-and-place and object stacking tasks, outperforming single-strategy approaches and a reinforcement learning baseline.
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The approach is significant for enabling robots to operate effectively in unpredictable and dynamic environments by adapting both actions and motions in real-time.
Introduction to Reactive Task and Motion Planning
Task and Motion Planning (TAMP), a significant area in robotics, involves creating plans that incorporate both discrete actions and continuous movements. Traditional approaches often assume a static environment and generate plans that are executed without considering changes that might occur in the real world. The dynamic nature of real-world scenarios, such as moving obstacles or unexpected disturbances, necessitates a more reactive planning approach that can adapt both high-level actions and low-level motions in real-time.
Active Inference Planner and Model Predictive Path Integral Controller
To address this need, an integrated planning strategy is introduced, combining two key components: an enhanced Active Inference planner (AIP) and a novel Multi-Modal Model Predictive Path Integral controller (M3P2I). The Active Inference planner is designed for high-level decision-making and can generate multiple alternative plans based on the current state and available actions. Each plan is linked to a different cost function, which is then evaluated by the M3P2I.
The M3P2I is based on a sampling approach that can handle complex dynamics and non-linearities, often characteristic of manipulation tasks. It extends this approach by enabling the parallel sampling of multiple plan alternatives, thus considering different possible actions at the same time. The AIP and M3P2I work together to create a robust control scheme that can handle uncertainties and adapt fluidly at both the action and motion level.
Algorithm and Implementation Details
The method operates by first converting continuous world states into symbolic observations, which are then processed by behavior trees and Active Inference to determine desired states or actions. The AIP then generates alternative action plans considering these desired states. Each plan is linked to specific cost functions, which are forwarded to the M3P2I for evaluation. The M3P2I samples control input sequences across these alternatives using a physics simulator, leading to multiple trajectory rollouts. By assigning weights to these rollouts based on their cost, the controller computes an optimal control input that effectively blends various strategic approaches.
Evaluation and Results
The proposed reactive TAMP method was tested in scenarios involving a push-and-place task and an object stacking task with a robotic manipulator. The results showed that combining the push and pull strategies using the M3P2I led to successful plan execution in cases where single-strategy approaches failed. Furthermore, the method performed favorably compared to a reinforcement learning baseline when faced with disturbances, despite requiring no prior training. Real-world experiments also demonstrated the approach's effectiveness in dealing with dynamic obstacles and disturbances.
In summary, by leveraging the combined strengths of the Active Inference planner and the Multi-Modal Model Predictive Path Integral controller within a reactive TAMP framework, this work presents a significant step towards enabling robots to perform complex tasks in unpredictable and dynamic environments.
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