Combining Optimal Control and Learning for Visual Navigation in Novel Environments

By Somil Bansal, Varun Tolani, Saurabh Gupta, Jitendra Malik, Claire Jennifer Tomlin. 2019

177 citations recorded by the source corpus.

Model-based control is a popular paradigm for robot navigation because it can leverage a known dynamics model to efficiently plan robust robot trajectories. However, it is challenging to use model-based methods in settings where the environment is a priori unknown and can only be observed partially through on-board sensors on the robot. In this work, we address this short-coming by coupling model-based control with learning-based perception. The learning-based perception module produces a series of waypoints that guide the robot to the goal via a collision-free path. These waypoints are used by a model-based planner to generate a smooth and dynamically feasible trajectory that is executed on the physical system using feedback control. Our experiments in simulated real-world cluttered environments and on an actual ground vehicle demonstrate that the proposed approach can reach goal locations more reliably and efficiently in novel environments as compared to purely geometric mapping-based or end-to-end learning-based alternatives. Our approach does not rely on detailed explicit 3D maps of the environment, works well with low frame rates, and generalizes well from simulation to the real world. Videos describing our approach and experiments are available on the project website.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering, Reinforcement Learning, Robotics & Control

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Citations in the corpus

Habitat 2.0: Training Home Assistants to Rearrange their Habitat (2021, background): “[18] Somil Bansal, Varun Tolani, Saurabh Gupta, Jitendra Malik, and Claire Tomlin.”
BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environments (2021, unknown): “Recently, signiﬁcant progress has been made in developing solutions to embodied AI problems such as (visual) navigation [1–5], interactive Q&A [6–10], instruction following [11–15], and manipulation [16–22].”
Neural Topological SLAM for Visual Navigation (2020, background): “For short-range locomotion tasks, purely reactive policies [3, 15, 22, 33] suffice.”
ReLMoGen: Integrating Motion Generation in Reinforcement Learning for Mobile Manipulation (2021, background): “Researchers have proposed learning-based solutions that map partial observations to waypoints [22, 23, 24, 25] or trajectories [26], thus bypassing the trajectory searching problem.”
The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark Towards Physically Realistic Embodied AI (2021, unknown): “Current state-of-the-art methods on embodied tasks mainly fall into two broad categories: end-to-end training of neural policies using RL [52, 26] or hierarchical RL [24, 2] and map building for path planning [20, 30, 7].”
Learning to Explore using Active Neural SLAM (2020, background): “For example in the context of navigation, Bansal et al. (2019) and Kaufmann et al. (2019) design modular policies for navigation, that interface learned policies with low-level feedback controllers.”
ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation (2020, background): “Researchers have proposed learning-based solutions that map partial observations to either waypoints [22, 23, 24, 25] or whole trajectories [26], thus bypassing the trajectory searching problem.”