iPlanner: Imperative Path Planning

Abstract

The problem of path planning has been studied for years. Classic planning pipelines, including perception, mapping, and path searching, can result in latency and compounding errors between modules. While recent studies have demonstrated the effectiveness of end-to-end learning methods in achieving high planning efficiency, these methods often struggle to match the generalization abilities of classic approaches in handling different environments. Moreover, end-to-end training of policies often requires a large number of labeled data or training iterations to reach convergence. In this paper, we present a novel Imperative Learning (IL) approach. This approach leverages a differentiable cost map to provide implicit supervision during policy training, eliminating the need for demonstrations or labeled trajectories. Furthermore, the policy training adopts a Bi-Level Optimization (BLO) process, which combines network update and metric-based trajectory optimization, to generate a smooth and collision-free path toward the goal based on a single depth measurement. The proposed method allows task-level costs of predicted trajectories to be backpropagated through all components to update the network through direct gradient descent. In our experiments, the method demonstrates around 4x faster planning than the classic approach and robustness against localization noise. Additionally, the IL approach enables the planner to generalize to various unseen environments, resulting in an overall 26-87% improvement in SPL performance compared to baseline learning methods.

arXiv comment9 pages, 11 figures, Robotics: Science and Systems (RSS) 2023

References in corpus (9)

• End to End Learning for Self-Driving Cars
• On Evaluation of Embodied Navigation Agents
• From Perception to Decision: A Data-driven Approach to End-to-end Motion Planning for Autonomous Ground Robots
• Matterport3D: Learning from RGB-D Data in Indoor Environments
• PyPose: A Library for Robot Learning with Physics-based Optimization
• Vision-Language Navigation with Self-Supervised Auxiliary Reasoning Tasks
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• Autonomous Exploration Development Environment and the Planning Algorithms
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Cited by in corpus (6)

• iSLAM: Imperative SLAM
• Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy
• iA*: Imperative Learning-based A* Search for Path Planning
• iMatching: Imperative Correspondence Learning
• iKap: Kinematics-aware Planning with Imperative Learning
• iWalker: Imperative Visual Planning for Walking Humanoid Robot