Source-linked AI summary
Motion Planning Networks
Ahmed H. Qureshi, Anthony Simeonov, Mayur J. Bency, Michael C. Yip
TL;DR
High-dimensional motion planning needs computationally tractable methods for generating collision-free paths. MPNet encodes obstacle point clouds and predicts paths from start and goal configurations, achieving consistently low computation times while generalizing to unseen workspaces.
Problem
Existing motion-planning methods become computationally difficult as planning dimensionality increases, despite the need for real-time planning in robotics applications.
Method
MPNet encodes obstacle point clouds into a latent space and uses a planning network with start and goal configurations to generate collision-free paths.
Results
MPNet’s mean computation time remains around 1 second across test cases and is about 40 and 20 times faster than Informed-RRT* and BIT*, respectively.
Takeaways & Limitations
MPNet generalizes to unseen start-goal configurations and new workspaces, while hybrid replanning retains computational gains with reported completeness guarantees.
Abstract
from arXiv · showhide
Fast and efficient motion planning algorithms are crucial for many state-of-the-art robotics applications such as self-driving cars. Existing motion planning methods become ineffective as their computational complexity increases exponentially with the dimensionality of the motion planning problem. To address this issue, we present Motion Planning Networks (MPNet), a neural network-based novel planning algorithm. The proposed method encodes the given workspaces directly from a point cloud measurement and generates the end-to-end collision-free paths for the given start and goal configurations. We evaluate MPNet on various 2D and 3D environments including the planning of a 7 DOF Baxter robot manipulator. The results show that MPNet is not only consistently computationally efficient in all environments but also generalizes to completely unseen environments. The results also show that the computation time of MPNet consistently remains less than 1 second in all presented experiments, which is significantly lower than existing state-of-the-art motion planning algorithms.
I. INTRODUCTION
MPNet addresses the need for computationally tractable motion planning in high-dimensional robotics by combining obstacle encoding with learned iterative planning. It is evaluated across diverse robots and environments, including unseen workspaces.
- Motion planning requires collision-free paths but becomes computationally challenging in high-dimensional applications such as autonomous driving and space exploration.
- MPNet combines an obstacle-point-cloud encoder with a planning network that predicts the next robot configuration from the current state, goal, and latent environment representation.
- The evaluation covers point-mass, rigid-body, and 7 DOF Baxter manipulation problems across multiple 2D and 3D environments.
- MPNet generalizes to unseen start-goal configurations and completely new workspaces, while its hybrid combination with RRT* achieves a 100% success rate across tested environments.
- Related work: Earlier neural planning approaches included reinforcement learning, imitation learning, and lookup-table path reuse, but their reported scope or memory requirements limited applicability.
III. PROBLEM DEFINITION
The paper formulates motion planning as finding a feasible path from an initial state to a goal region entirely within obstacle-free space. MPNet uses offline neural-model training before online path generation.
- A feasible solution path connects the initial state to the goal region and lies entirely in the obstacle-free state space.
- MPNet has separate offline training and online path-generation phases.
- An encoder embeds obstacle point clouds into a latent space, while a planning network uses that embedding with start and goal configurations.
- The encoder can be trained with an encoder-decoder reconstruction objective or end-to-end with the planning network.
- The reconstruction objective uses obstacle point-cloud data, encoder and decoder parameters, a penalty coefficient, and reconstructed point clouds.
2) Planning Network:
The planning network predicts successive robot states from an encoded obstacle space, current state, and goal, while online planning uses these models to generate feasible paths.
- Pnet predicts the next robot state from the obstacle encoding, current state, and goal state, with the prediction intended to move the robot toward the goal region.
- Pnet is trained by minimizing mean-squared error between predicted next states and states in feasible, near-optimal expert paths.
- The loss averages prediction errors across the paths and their lengths in the training dataset.
- Online planning combines the trained models with an incremental bidirectional path-generation heuristic to connect start and goal states in cluttered environments.
- The obstacle point cloud is encoded into latent representation Z before being used by the planning network.
1) Enet:
The network-based planner uses a stochastic feed-forward predictor and path-processing checks to construct and validate motion trajectories.
- Neural planner: Pnet predicts the next robot state from the obstacle representation, current state, and goal state.
- Neural planner: Dropout in hidden layers introduces stochasticity during online path generation.
- Path processing: The lazy state contraction procedure connects directly connectable non-consecutive states and removes intermediate lazy states.
- Path validation: steerTo checks whether a discretized straight trajectory between two states remains entirely collision-free.
- Path validation: The path-validation procedure checks whether the end-to-end path formed from consecutive states lies entirely in obstacle-free space.
6) Neural Planner:
The neural planner incrementally grows two paths from the start and goal, alternating extensions until they can be connected into an end-to-end path.
- The heuristic takes obstacle representation Z, start state, and goal state as inputs, then outputs a connecting path.
- Pnet generates a new state from the start-side path toward the goal, after which the planner checks whether the two paths are connectable.
- When the paths connect, the planner returns their concatenation as the final end-to-end path.
- If they do not connect, the algorithm swaps the path roles and repeats, alternating extensions so the trajectories march toward each other.
7) Replanning:
MPNet supports neural and hybrid replanning when consecutive states in a path are not connectable, using recursive refinement or a classical planner for infeasible segments.
- The replanning procedure checks every consecutive state pair in a path and generates a replacement path for any pair that is not connectable.
- Neural Replanning: Neural replanning recursively generates a coarse path, then refines non-connectable segments by calling itself for a fixed number of steps.
- Hybrid Replanning: Hybrid replanning first applies neural replanning for a fixed number of steps and tests the resulting path for feasibility.
- Hybrid Replanning: If the neural path is infeasible, a classical motion planner connects the non-connectable states.
V. IMPLEMENTATION DETAILS
MPNet was implemented in PyTorch, while benchmark planners and Baxter-specific comparisons used different software stacks and implementations.
- The neural models were implemented in PyTorch.
- For non-Baxter environments, Informed-RRT* and BIT* were implemented in Python and compared against MPNet CPU time.
- Baxter environments used MoveIt! and ROS, with BIT* implemented in C++ through OMPL for comparison.
A. Data Collection
The evaluation datasets cover multiple workspace types, large RRT*-generated path collections, unseen workspaces, and simulated Baxter environments with real-robot execution.
- 110 workspaces were generated for each simple 2D, rigid-body, complex 2D, and 3D case.
- Each workspace contained 5000 collision-free, near-optimal paths generated using RRT*.
- The training dataset used 100 workspaces with 4000 paths per workspace, while testing included seen and completely unseen workspace datasets.
- The unseen-Xobs dataset contained 10 unseen workspaces, each with 2000 unseen start and goal configurations.
- Baxter experiments used ten challenging simulated environments, with 900 training paths and 100 testing paths per environment, plus real-robot execution.
B. Models Architecture
MPNet combines learned environment encoding with neural planning and is evaluated against classical planners across diverse robot and environment settings. The experiments report substantially lower computation time while maintaining high path-finding accuracy.
- Architecture and evaluation: MPNet uses point-cloud obstacle encoding and planning networks to support motion planning for point-mass, rigid-body, and Baxter 7 DOF robots.The encoder produces an environment representation, while the planning network predicts configurations conditioned on the current, goal, and encoded obstacle states.
- Results: MPNet generated near-optimal paths for point-mass and rigid-body robots in considerably less time than RRT*.The comparison uses mean computational times denoted tMP and tR for MPNet and RRT*, respectively.
- Results: Around 1 second was the mean computation time for MPNet with neural and hybrid replanning across all test cases.The comparison covered seen-Xobs and unseen-Xobs datasets and reported mean times with standard deviations.
- Results: 40 and 20 times faster were MPNet's average times than Informed-RRT* and BIT*, respectively, across all test cases.MPNet maintained low computational time irrespective of planning-problem dimensionality.
VII. DISCUSSION
MPNet uses stochastic neural replanning to refine coarse paths, while hybrid replanning invokes a classical planner when neural output is infeasible. The hybrid approach addresses the lack of theoretical guarantees in neural networks and achieved consistent success across tested environments.
- Neural replanning: Dropout makes neural replanning stochastic, so repeated replanning can generate different paths for the same start and goal states.The replanning procedure recursively refines non-connectable consecutive states of a coarse path until a feasible solution is found or a loop limit is reached.
- Hybrid replanning: Hybrid replanning tests the neural path for feasibility and uses a classical motion planner to connect non-connectable states when necessary.This combines fixed-step neural replanning with classical repair of infeasible path segments.
- Discussion: 100% success was achieved consistently by the hybrid algorithm across all tested environments while retaining computational gains.The hybrid method combines MPNet with RRT* in response to the absence of theoretical performance guarantees for neural networks.
C. Computational Complexity
MPNet's neural replanner has fixed-step execution, giving O(1) complexity, while hybrid replanning has a classical-planner-dependent worst case. The proposed method reports about 1-second execution and includes extensions for dynamic environments as future work.
- Complexity: O(1) is the complexity of neural replanning because it executes for a fixed number of steps.Neural-network execution and the fixed-iteration lazy state contraction contribute constant-time components; lazy state contraction helps generate near-optimal paths but is not essential.
- Complexity: O(nlogn) is the hybrid replanner's worst-case complexity, while its best-case complexity is O(1) when classical replanning is unnecessary.Here n is the number of samples in the RRT* tree.
- Complexity: More than 97% of cases were handled by MPNet: NR with O(1) operation, leaving nearly 3% for RRT* execution on small path segments.Running RRT* only on small segments makes its practical cost much lower than applying it to the full planning problem.
- Implications: About 1 second was MPNet's mean execution time in all presented experiments.The paper also reports generalization to new unseen obstacle locations and completeness guarantees.
- Future work: Kinodynamic planning in dynamically changing environments remains a future-work direction for MPNet.The authors also propose combining MPNet with proxy collision checkers such as Fastron in future work.